Managing Error Recovery Data in a Dispersed Storage Network

Abstract

A method for managing error recovery data in a dispersed storage network begins with a storage network processing module receiving a write request for an encoded data slice of a set of encoded data slices, where data is dispersed in accordance with dispersed error encoding parameters to produce a set of encoded data slices. The method continues with the storage network processing module generating parity data for the encoded data slice and sending the encoded data slice to a first storage unit of a set of storage units. Finally, the method continues with the storage network processing module sending the parity data for the encoded data slice to a second storage unit of a set of storage units.

Description

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and more particularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/or store data. Such computing devices range from wireless smart phones, laptops, tablets, personal computers (PC), work stations, and video game devices, to data centers that support millions of web searches, stock trades, or on-line purchases every day. In general, a computing device includes a central processing unit (CPU), a memory system, user input/output interfaces, peripheral device interfaces, and an interconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using “cloud computing” to perform one or more computing functions (e.g., a service, an application, an algorithm, an arithmetic logic function, etc.) on behalf of the computer. Further, for large services, applications, and/or functions, cloud computing may be performed by multiple cloud computing resources in a distributed manner to improve the response time for completion of the service, application, and/or function. For example, Hadoop is an open source software framework that supports distributed applications enabling application execution by thousands of computers.

In addition to cloud computing, a computer may use “cloud storage” as part of its memory system. As is known, cloud storage enables a user, via its computer, to store files, applications, etc. on an Internet storage system. The Internet storage system may include a RAID (redundant array of independent disks) system and/or a dispersed storage system that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed or distributed storage network (DSN) in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing core in accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storage error encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an error encoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an error encoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of an encoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storage error decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an error decoding function in accordance with the present invention;

FIG. 9A is a flowchart illustrating an example of storing key shares in accordance with the present invention;

FIG. 9B is a diagram illustrating an example of a key share storage table in accordance with the present invention;

FIG. 9C is a flowchart illustrating an example of generating a signature in accordance with the present invention;

FIG. 9D is a flowchart illustrating an example of generating a signature contribution in accordance with the present invention; and

FIG. 10 is a flowchart illustrating another example of generating a signature contribution in accordance with the invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, or distributed, storage network (DSN) 10 that includes a plurality of computing devices 12-16, a managing unit 18, an integrity processing unit 20, and a DSN memory 22. The components of the DSN 10 are coupled to a network 24, which may include one or more wireless and/or wire lined communication systems; one or more non-public intranet systems and/or public internet systems; and/or one or more local area networks (LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may be located at geographically different sites (e.g., one in Chicago, one in Milwaukee, etc.), at a common site, or a combination thereof. For example, if the DSN memory 22 includes eight storage units 36, each storage unit is located at a different site. As another example, if the DSN memory 22 includes eight storage units 36, all eight storage units are located at the same site. As yet another example, if the DSN memory 22 includes eight storage units 36, a first pair of storage units are at a first common site, a second pair of storage units are at a second common site, a third pair of storage units are at a third common site, and a fourth pair of storage units are at a fourth common site. Note that a DSN memory 22 may include more or less than eight storage units 36. Further note that each storage unit 36 includes a computing core (as shown in FIG. 2, or components thereof) and a plurality of memory devices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and the integrity processing unit 20 include a computing core 26, which includes network interfaces 30-33. Computing devices 12-16 may each be a portable computing device and/or a fixed computing device. A portable computing device may be a social networking device, a gaming device, a cell phone, a smart phone, a digital assistant, a digital music player, a digital video player, a laptop computer, a handheld computer, a tablet, a video game controller, and/or any other portable device that includes a computing core. A fixed computing device may be a computer (PC), a computer server, a cable set-top box, a satellite receiver, a television set, a printer, a fax machine, home entertainment equipment, a video game console, and/or any type of home or office computing equipment. Note that each of the managing unit 18 and the integrity processing unit 20 may be separate computing devices, may be a common computing device, and/or may be integrated into one or more of the computing devices 12-16 and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to support one or more communication links via the network 24 indirectly and/or directly. For example, interface 30 supports a communication link (e.g., wired, wireless, direct, via a LAN, via the network 24, etc.) between computing devices 14 and 16. As another example, interface 32 supports communication links (e.g., a wired connection, a wireless connection, a LAN connection, and/or any other type of connection to/from the network 24) between computing devices 12 and 16 and the DSN memory 22. As yet another example, interface 33 supports a communication link for each of the managing unit 18 and the integrity processing unit 20 to the network 24.

Computing devices 12 and 16 include a dispersed storage (DS) client module 34, which enables the computing device to dispersed storage error encode and decode data (e.g., data 40) as subsequently described with reference to one or more of FIGS. 3-8. In this example embodiment, computing device 16 functions as a dispersed storage processing agent for computing device 14. In this role, computing device 16 dispersed storage error encodes and decodes data on behalf of computing device 14. With the use of dispersed storage error encoding and decoding, the DSN 10 is tolerant of a significant number of storage unit failures (the number of failures is based on parameters of the dispersed storage error encoding function) without loss of data and without the need for a redundant or backup copies of the data. Further, the DSN 10 stores data for an indefinite period of time without data loss and in a secure manner (e.g., the system is very resistant to unauthorized attempts at accessing the data).

In operation, the managing unit 18 performs DS management services. For example, the managing unit 18 establishes distributed data storage parameters (e.g., vault creation, distributed storage parameters, security parameters, billing information, user profile information, etc.) for computing devices 12-14 individually or as part of a group of user devices. As a specific example, the managing unit 18 coordinates creation of a vault (e.g., a virtual memory block associated with a portion of an overall namespace of the DSN) within the DSN memory 22 for a user device, a group of devices, or for public access and establishes per vault dispersed storage (DS) error encoding parameters for a vault. The managing unit 18 facilitates storage of DS error encoding parameters for each vault by updating registry information of the DSN 10, where the registry information may be stored in the DSN memory 22, a computing device 12-16, the managing unit 18, and/or the integrity processing unit 20.

The managing unit 18 creates and stores user profile information (e.g., an access control list (ACL)) in local memory and/or within memory of the DSN memory 22. The user profile information includes authentication information, permissions, and/or the security parameters. The security parameters may include encryption/decryption scheme, one or more encryption keys, key generation scheme, and/or data encoding/decoding scheme.

The managing unit 18 creates billing information for a particular user, a user group, a vault access, public vault access, etc. For instance, the managing unit 18 tracks the number of times a user accesses a non-public vault and/or public vaults, which can be used to generate a per-access billing information. In another instance, the managing unit 18 tracks the amount of data stored and/or retrieved by a user device and/or a user group, which can be used to generate a per-data-amount billing information.

As another example, the managing unit 18 performs network operations, network administration, and/or network maintenance. Network operations includes authenticating user data allocation requests (e.g., read and/or write requests), managing creation of vaults, establishing authentication credentials for user devices, adding/deleting components (e.g., user devices, storage units, and/or computing devices with a DS client module 34) to/from the DSN 10, and/or establishing authentication credentials for the storage units 36. Network administration includes monitoring devices and/or units for failures, maintaining vault information, determining device and/or unit activation status, determining device and/or unit loading, and/or determining any other system level operation that affects the performance level of the DSN 10. Network maintenance includes facilitating replacing, upgrading, repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missing encoded data slices. At a high level, the integrity processing unit 20 performs rebuilding by periodically attempting to retrieve/list encoded data slices, and/or slice names of the encoded data slices, from the DSN memory 22. For retrieved encoded slices, they are checked for errors due to data corruption, outdated version, etc. If a slice includes an error, it is flagged as a ‘bad’ slice. For encoded data slices that were not received and/or not listed, they are flagged as missing slices. Bad and/or missing slices are subsequently rebuilt using other retrieved encoded data slices that are deemed to be good slices to produce rebuilt slices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core 26 that includes a processing module 50, a memory controller 52, main memory 54, a video graphics processing unit 55, an input/output (IO) controller 56, a peripheral component interconnect (PCI) interface 58, an IO interface module 60, at least one IO device interface module 62, a read only memory (ROM) basic input output system (BIOS) 64, and one or more memory interface modules. The one or more memory interface module(s) includes one or more of a universal serial bus (USB) interface module 66, a host bus adapter (HBA) interface module 68, a network interface module 70, a flash interface module 72, a hard drive interface module 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operating system (OS) file system interface (e.g., network file system (NFS), flash file system (FFS), disk file system (DFS), file transfer protocol (FTP), web-based distributed authoring and versioning (WebDAV), etc.) and/or a block memory interface (e.g., small computer system interface (SCSI), internet small computer system interface (iSCSI), etc.). The DSN interface module 76 and/or the network interface module 70 may function as one or more of the interface 30-33 of FIG. 1. Note that the IO device interface module 62 and/or the memory interface modules 66-76 may be collectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storage error encoding of data. When a computing device 12 or 16 has data to store it disperse storage error encodes the data in accordance with a dispersed storage error encoding process based on dispersed storage error encoding parameters. The dispersed storage error encoding parameters include an encoding function (e.g., information dispersal algorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding, non-systematic encoding, on-line codes, etc.), a data segmenting protocol (e.g., data segment size, fixed, variable, etc.), and per data segment encoding values. The per data segment encoding values include a total, or pillar width, number (T) of encoded data slices per encoding of a data segment (i.e., in a set of encoded data slices); a decode threshold number (D) of encoded data slices of a set of encoded data slices that are needed to recover the data segment; a read threshold number (R) of encoded data slices to indicate a number of encoded data slices per set to be read from storage for decoding of the data segment; and/or a write threshold number (W) to indicate a number of encoded data slices per set that must be accurately stored before the encoded data segment is deemed to have been properly stored. The dispersed storage error encoding parameters may further include slicing information (e.g., the number of encoded data slices that will be created for each data segment) and/or slice security information (e.g., per encoded data slice encryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as the encoding function (a generic example is shown in FIG. 4 and a specific example is shown in FIG. 5); the data segmenting protocol is to divide the data object into fixed sized data segments; and the per data segment encoding values include: a pillar width of 5, a decode threshold of 3, a read threshold of 4, and a write threshold of 4. In accordance with the data segmenting protocol, the computing device 12 or 16 divides the data (e.g., a file (e.g., text, video, audio, etc.), a data object, or other data arrangement) into a plurality of fixed sized data segments (e.g., 1 through Y of a fixed size in range of Kilobytes to Terabytes or more). The number of data segments created is dependent of the size of the data and the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a data segment using the selected encoding function (e.g., Cauchy Reed-Solomon) to produce a set of encoded data slices. FIG. 4 illustrates a generic Cauchy Reed-Solomon encoding function, which includes an encoding matrix (EM), a data matrix (DM), and a coded matrix (CM). The size of the encoding matrix (EM) is dependent on the pillar width number (T) and the decode threshold number (D) of selected per data segment encoding values. To produce the data matrix (DM), the data segment is divided into a plurality of data blocks and the data blocks are arranged into D number of rows with Z data blocks per row. Note that Z is a function of the number of data blocks created from the data segment and the decode threshold number (D). The coded matrix is produced by matrix multiplying the data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encoding with a pillar number (T) of five and decode threshold number of three. In this example, a first data segment is divided into twelve data blocks (D1-D12). The coded matrix includes five rows of coded data blocks, where the first row of X11-X14 corresponds to a first encoded data slice (EDS 1_1), the second row of X21-X24 corresponds to a second encoded data slice (EDS 2_1), the third row of X31-X34 corresponds to a third encoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to a fourth encoded data slice (EDS 4_1), and the fifth row of X51-X54 corresponds to a fifth encoded data slice (EDS 5_1). Note that the second number of the EDS designation corresponds to the data segment number.

Returning to the discussion of FIG. 3, the computing device also creates a slice name (SN) for each encoded data slice (EDS) in the set of encoded data slices. A typical format for a slice name 80 is shown in FIG. 6. As shown, the slice name (SN) 80 includes a pillar number of the encoded data slice (e.g., one of 1-T), a data segment number (e.g., one of 1-Y), a vault identifier (ID), a data object identifier (ID), and may further include revision level information of the encoded data slices. The slice name functions as, at least part of, a DSN address for the encoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces a plurality of sets of encoded data slices, which are provided with their respective slice names to the storage units for storage. As shown, the first set of encoded data slices includes EDS 1_1 through EDS 5_1 and the first set of slice names includes SN 1_1 through SN 5_1 and the last set of encoded data slices includes EDS 1_Y through EDS 5_Y and the last set of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storage error decoding of a data object that was dispersed storage error encoded and stored in the example of FIG. 4. In this example, the computing device 12 or 16 retrieves from the storage units at least the decode threshold number of encoded data slices per data segment. As a specific example, the computing device retrieves a read threshold number of encoded data slices.

To recover a data segment from a decode threshold number of encoded data slices, the computing device uses a decoding function as shown in FIG. 8. As shown, the decoding function is essentially an inverse of the encoding function of FIG. 4. The coded matrix includes a decode threshold number of rows (e.g., three in this example) and the decoding matrix in an inversion of the encoding matrix that includes the corresponding rows of the coded matrix. For example, if the coded matrix includes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2, and 4, and then inverted to produce the decoding matrix.

FIG. 9A is a flowchart illustrating an example of storing key shares. FIG. 9A is a flowchart illustrating an example of storing key shares. Storage of secrets in multiple storage nodes can be used to prevent the capture of the ciphertext and the subsequent cryptanalysis on that ciphertext. In such a system decrypting an encrypted message or signing a message involves several parties (more than some threshold number) who must cooperate in the decryption or signature protocol. In an example, a message is encrypted using a public key and the corresponding private key is shared among the participating parties. Multiple schemes and algorithms are available for implementing these “threshold signature” cryptosystems. For example, one such system is based on distributing secret shares of a private key (see, for example, “Practical Threshold Signatures” by Victor Shoup, et. al). Another example is based on the Boneh-Lynn-Shacham (BLS) Signature Scheme (using threshold signatures based on bi-linear pairings). Yet another example is the various threshold signature schemes where shares are produced from linear functions applied to a private key. Many more examples will be readily apparent to those skilled in the related technology space. In an example, some signature are algorithms distinct from Rivest-Shamir-Adleman (RSA) type cryptosystems.

In many of the systems referred to above the participants/nodes need not be pre-established before the threshold cryptosystem(s) are employed. Instead, a single share value generated from a key is sufficient to be used with virtually any combination of servers used to produce a particular signature.

The method begins with step 144 where a processing module determines a private key (e.g., an encryption key d utilized in an encryption algorithm). Such a determination may be based on one or more of generating a key pair (e.g., a private key and a key), receiving the private key, a query, a lookup, and a user input. The method continues at step 146 where the processing module determines sharing function parameters. Such sharing function parameters includes one or more of a width (e.g., a number of storage nodes w), a decode threshold (e.g., a number of key shares k), a number of shares sets (e.g., w choose k), a public modulus n, security function constants p and q (e.g., large primes such that p*q=n), an encryption algorithm identifier, and a decryption algorithm identifier. Such a determination may be based on one or more of a list, a predetermination, a query, a performance level indicator, a reliability level requirement, a message, and a command. For example, the processing module determines w=4, k=2, a number of shares sets=(4 choose 2)=6, a value for n, and a value for p based on a lookup and generates a value for q in accordance with p*q=n.

The method continues at step 148 where the processing module determines a plurality of storage nodes for storing key shares. Such plurality of storage nodes includes two or more of a user device, a dispersed storage (DS) unit, a storage server, and a memory device. Such a determination may be based on one or more of the sharing function parameters, a list, a predetermination, a query, a performance level indicator, a message, and a command. For example, the processing module determines the plurality of storage nodes to include 4 DS units when the sharing function parameters include a width w=4 and a performance level indicator indicates that a performance level of the 4 DS units compares favorably to a performance level threshold.

The method continues at step 150 where the processing module generates one or more sets of key shares to include the number of shares sets. For example, the processing module generates 6 sets of key shares when the width=4 and the decode threshold k=2 (e.g., 4 choose 2=6). Such a generation produces a set of key shares for each combination of a decode threshold k number of key shares stored in the width w number of storage nodes. Such generation of each set of key shares includes generation in accordance with a formula (x+y+z) mod Φ(n)=private key d, wherein Φ(n)=(p−1)*(q−1), and x, y, z represent key shares of a corresponding key share set when a number of key shares is three. For example, the processing module randomly chooses values for key shares y and z of a corresponding key share set and generates a value for key share x in accordance with the formula.

In an example of generating key share sets, the processing module generates 10 key shares sets to include a first key share set that includes a key share x1 to store in DS unit 1, a key share yl to store in DS unit 2, and a key share z1 to store in DS unit 3, a second key share set that includes a key share x2 to store in DS unit 1, a key share y2 to store in DS unit 2, and a key share z2 to store in DS unit 4, a third key share set that includes a key share x3 to store in DS unit 1, a key share y3 to store in DS unit 2, and a key share z3 to store in DS unit 5, a fourth key share set that includes a key share x4 to store in DS unit 1, a key share y4 to store in DS unit 3, and a key share z4 to store in DS unit 5, a fifth key share set that includes a key share x5 to store in DS unit 1, a key share y5 to store in DS unit 4, and a key share z5 to store in DS unit 5, a sixth key share set that includes a key share x6 to store in DS unit 2, a key share y6 to store in DS unit 3, and a key share z6 to store in DS unit 4, a seventh key share set that includes a key share x7 to store in DS unit 2, a key share y7 to store in DS unit 3, and a key share z7 to store in DS unit 5, an eighth key share set that includes a key share x8 to store in DS unit 2, a key share y7 to store in DS unit 4, and a key share z7 to store in DS unit 5, a ninth key share set that includes a key share x9 to store in DS unit 3, a key share y9 to store in DS unit 4, and a key share z9 to store in DS unit 5, and a 10th key share set that includes a key share x10 to store in DS unit 1, a key share y10 to store in DS unit 3, and a key share z10 to store in DS unit 4 when a number storage nodes is 5 and a decode threshold is 3.

The method continues at step 152 where the processing module outputs the one or more sets of key shares to the plurality of storage nodes. In addition, the processing module may output one or more of the sharing function parameters to each storage node of the plurality of storage nodes. For example, the processing module sends the public modulus n to each storage node of the plurality of storage nodes. The method continues at step 154 where the processing module destroys the private key d. Note that such destroying of the private key may provide the system with a security performance improvement. A method to generate a signature based on stored shared keys is described in greater detail with reference to FIG. 9C. A method to generate a signature contribution (e.g., by a storage node) is described in greater detail with reference to FIG. 9D.

FIG. 9B is a diagram illustrating an example of a key share storage table 156 that includes a share set field 158, a node combination field 160, and a key share per storage node field 162. Such a share set field 158 includes a share sets number of share set identifiers of corresponding key share sets. For example, the share set field 158 includes 6 share set identifiers 1-6 when a number of storage nodes w=4, a decode threshold k=2, and the share sets number of share set identifiers is w choose k (e.g., 4 choose 2=6). Such a node combination field 160 includes a share sets number of node combination entries, wherein each node combination entry corresponds to a combination of a decode threshold number of storage node identifiers. For example, the node combination field 160 includes 6 node combination entries including A-B, A-C, A-D, B-C, B-D, and C-D when storage nodes A-D are utilized to store a share sets number (e.g., 6) of a decode threshold number (e.g., 2) of key shares.

Such a key share per storage node field 162 includes a share sets number of storage node fields, wherein each storage node field corresponds to a storage node of a number of storage nodes w utilized to store the key shares. Each storage node field includes a key share identifier utilized to identify a key share of an associated share set that is stored in a storage node corresponding to the storage node field. For example, share set 1 includes utilization of storage node combination A-B such that key share x1 is stored in storage unit A and key share y1 is stored in storage node B, share set 2 includes utilization of storage node combination A-C such that key share x2 is stored in storage unit A and key share y2 is stored in storage node C, share set 3 includes utilization of storage node combination A-D such that key share x3 is stored in storage unit A and key share y3 is stored in storage node D, share set 4 includes utilization of storage node combination B-C such that key share x4 is stored in storage unit B and key share y4 is stored in storage node C, share set 5 includes utilization of storage node combination B-D such that key share x5 is stored in storage unit B and key share y5 is stored in storage node D, and share set 6 includes utilization of storage node combination C-D such that key share x6 is stored in storage unit C and key share y6 is stored in storage node D. Each set of key shares may be generated in accordance with a formula (x+y) mod Φ(n)=private key d, wherein Φ(n)=(p−1)*(q−1). For example, a value of key share y1 is chosen randomly and a value for key share x1 is generated in accordance with the formula.

FIG. 9C is a flowchart illustrating an example of generating a signature, which include similar steps to FIG. 9A. The method begins with step 170 where a processing module obtains a message to sign. Such a message may include a data file, data object, a data file hash, a data object cache, a data block, and a hash of a data block, a hash of a payload. Such a payload may include one or more of registry information, key information, encryption algorithm information, a device certificate, a user certificate, and a system element identifier (ID). The method continues with step 172 where the processing module determines sharing function parameters.

The method continues at step 174 where the processing module determines a candidate plurality of storage nodes for retrieving signature contributions. Such a determination may be based on one or more of a list of storage nodes utilized to store key shares, a lookup, a command, a query, and a message. The method continues at step 176 where the processing module selects a storage node group of the candidate plurality of storage nodes for retrieving signature contributions. Such a storage node group includes at least a decode threshold number of storage nodes. Such a determination may be based on one or more of a storage node status indicator, a storage node performance level indicator, a retrieval history indicator, a security indicator, a query, a command, a key share storage table lookup, and a message. For example, the processing module determines the storage node group to include storage nodes A and D based on the key share storage table lookup and a retrieval history indicator for storage nodes A and D that indicates a favorable retrieval history level.

The method continues at step 178 where the processing module outputs a plurality of signature contribution requests to the storage node group. Such a request of the plurality requests may include one or more of a share set ID, a storage node group ID, the message to sign, and at least one parameter of the sharing function parameters. For example, the processing module outputs signature contribution requests to storage nodes A and B, wherein each signature contribution request includes a share set ID of A-D, a public modulus n, and a message to sign that includes a hash of a device certificate.

The method continues at step 180 where the processing module receives one or more signature contributions. The method continues at step 182 where the processing module determines whether all of a decode threshold number of signature contributions has been received. The method repeats back to step 176 where the processing module selects the storage node group when the processing module determines that all of the decode threshold number of signature contributions has not been received. The method continues to step 182 when the processing module determines that all of the decode threshold number of signature contributions has been received. In step 184 the processing module generates a signature based on the signature contributions. Such generation includes generating the signature in accordance with the formula signature s=((signature contribution A)*(signature contribution B)) mod n, when the decode threshold is 2 and signature contributions A and B have been received (e.g., from storage nodes A and B). In addition, the processing module may verify the signature. Such verification includes decrypting the signature utilizing an associated public key to produce a decrypted signature and comparing the decrypted signature to the message to sign (e.g., the hash of the device certificate). The processing module determines that the signature is valid when the comparisons favorable (e.g., substantially the same). The method repeats back to step 176 where the processing module selects a storage node group to select a different storage node group and repeat the process to re-generate the signature when the processing module determines that the signature is not valid.

FIG. 9D is a flowchart illustrating an example of generating a signature contribution. The method begins with step 186 where a processing module receives a signature contribution request. The method continues at step 188 where the processing module retrieves a key share based on sharing function parameters. The processing module obtains the sharing function parameters based on one or more of receiving the sharing function parameters, extracting the sharing function parameters from the request, a lookup, a message, a command, and a predetermination. For example, the processing module extracts the sharing function parameters from the signature contribution request to include a key share ID. Such retrieving of the key share includes obtaining the key share ID and retrieving the key share based on the key share ID. For example, the processing module obtains key share ID x3, determines a storage node A memory location based on the key share ID x3, and retrieves the key share x3 from a memory of the storage node at the storage node memory location.

As another example, the processing module extracts the sharing function parameters from the signature contribution request to include a share set ID. Such retrieving of the key share includes obtaining the share set ID and retrieving the key share based on the share set ID and a key share storage table lookup. For example, the processing module obtains share set ID A-D, determines a key share ID of x3 based on a key share storage table lookup utilizing the share set ID A-D as an index, determines a storage node A memory location based on the key share ID x3, and retrieves the key share x3 from a memory of the storage node at the storage node memory location.

The method continues at step 190 where the processing module generates a signature contribution based on the key share and a message to sign m. Obtaining the message to sign m includes at least one of extracting the message to sign m from the signature contribution request and generating a hash of a payload from the signature contribution request. Such generation of the signature contribution includes generating the signature contribution in accordance with the formula signature contribution=mx mod n. For example, the processing module generates the signature contribution in accordance with signature contribution A=mx3 mod n, when the processing module is associated with storage node A and the key share is x3. The method continues at step 192 where the processing module outputs the signature contribution.

FIG. 10 is a flowchart illustrating another example of generating a signature contribution, which include steps similar to FIG. 9D. The method begins with step 200 where a processing module receives a signature contribution request that includes a payload. The method continues at step 202 where the processing module logs the signature contribution request. Such logging includes extracting request information from the signature contribution request, obtaining a user identifier (ID), obtaining a vault ID, obtaining a timestamp, aggregating the request information, the user ID, the vault ID, and the timestamp to produce logging information, and facilitating storing of the logging information.

The method continues at step 204 where the processing module determines whether timing of the signature contribution request compares favorably to a timing template. For example, the processing module determines that the comparison is favorable when a difference between the timestamp associated with the signature contribution request and a timestamp associated with a previous signature contribution request is greater than a time threshold of the timing template. The method branches to steps 204 where the processing module determines whether the request compares favorably to a functionality template, and when the processing module determines that the timing of the request does not compare favorably to the timing template the method continues to step 206 when the processing module determines that the timing of the request compares unfavorably to the timing template. The method continues at step 212 where the processing module outputs a request rejection message. Such a request rejection message includes one or more of the signature contribution requests, the logging information, the timestamp associated with the signature contribution request, and an error code. The processing module may output the request rejection message to one or more of a requester, a dispersed storage (DS) imaging unit, a DS processing unit, a DS unit, and a user device.

The method continues at step 208 where the processing module determines whether the signature contribution request compares favorably to the functionality template. Such a determination may be based on one or more of the payload, a payload analysis, and a comparison of the payload analysis to the functionality template. For example, the processing module determines that the request compares favorably to the functionality template when the processing module determines that a registry value of the payload does not conflict with a current registry value. As another example, the processing module determines that the request compares favorably to the functionality template when the payload is not a certificate authority certificate. As yet another example, the processing module determines that the request compares favorably to the functionality template when an internet protocol (IP) address associated with a requester of the request does not compare unfavorably to an unfavorable IP address list.

The method branches to step 188 of FIG. 9D where the processing module retrieves a key share based on sharing function parameters when the processing module determines that the request compares favorably to the functionality template. The method continues to step 210 when the processing module determines that the request compares unfavorably to the functionality template. The method continues at step 212 where the processing module outputs the request rejection message. The method continues with the steps of FIG. 9D where the processing module retrieves the key share based on sharing function parameters, generates a signature result based on the key share and message to sign, and outputs the signature result.

It is noted that terminologies as may be used herein such as bit stream, stream, signal sequence, etc. (or their equivalents) have been used interchangeably to describe digital information whose content corresponds to any of a number of desired types (e.g., data, video, speech, text, graphics, audio, etc. any of which may generally be referred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately” provides an industry-accepted tolerance for its corresponding term and/or relativity between items. For some industries, an industry-accepted tolerance is less than one percent and, for other industries, the industry-accepted tolerance is 10 percent or more. Other examples of industry-accepted tolerance range from less than one percent to fifty percent. Industry-accepted tolerances correspond to, but are not limited to, component values, integrated circuit process variations, temperature variations, rise and fall times, thermal noise, dimensions, signaling errors, dropped packets, temperatures, pressures, material compositions, and/or performance metrics. Within an industry, tolerance variances of accepted tolerances may be more or less than a percentage level (e.g., dimension tolerance of less than +/−1%). Some relativity between items may range from a difference of less than a percentage level to a few percent. Other relativity between items may range from a difference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via an intervening item (e.g., an item includes, but is not limited to, a component, an element, a circuit, and/or a module) where, for an example of indirect coupling, the intervening item does not modify the information of a signal but may adjust its current level, voltage level, and/or power level. As may further be used herein, inferred coupling (i.e., where one element is coupled to another element by inference) includes direct and indirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operable to”, “coupled to”, or “operably coupled to” indicates that an item includes one or more of power connections, input(s), output(s), etc., to perform, when activated, one or more its corresponding functions and may further include inferred coupling to one or more other items. As may still further be used herein, the term “associated with”, includes direct and/or indirect coupling of separate items and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that a comparison between two or more items, signals, etc., provides a desired relationship. For example, when the desired relationship is that signal 1 has a greater magnitude than signal 2, a favorable comparison may be achieved when the magnitude of signal 1 is greater than that of signal 2 or when the magnitude of signal 2 is less than that of signal 1. As may be used herein, the term “compares unfavorably”, indicates that a comparison between two or more items, signals, etc., fails to provide the desired relationship.

As may be used herein, one or more claims may include, in a specific form of this generic form, the phrase “at least one of a, b, and c” or of this generic form “at least one of a, b, or c”, with more or less elements than “a”, “b”, and “c”. In either phrasing, the phrases are to be interpreted identically. In particular, “at least one of a, b, and c” is equivalent to “at least one of a, b, or c” and shall mean a, b, and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and “b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processing circuit”, “processor”, “processing circuitry”, and/or “processing unit” may be a single processing device or a plurality of processing devices. Such a processing device may be a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. The processing module, module, processing circuit, processing circuitry, and/or processing unit may be, or further include, memory and/or an integrated memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of another processing module, module, processing circuit, processing circuitry, and/or processing unit. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. Note that if the processing module, module, processing circuit, processing circuitry, and/or processing unit includes more than one processing device, the processing devices may be centrally located (e.g., directly coupled together via a wired and/or wireless bus structure) or may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). Further note that if the processing module, module, processing circuit, processing circuitry and/or processing unit implements one or more of its functions via a state machine, analog circuitry, digital circuitry, and/or logic circuitry, the memory and/or memory element storing the corresponding operational instructions may be embedded within, or external to, the circuitry comprising the state machine, analog circuitry, digital circuitry, and/or logic circuitry. Still further note that, the memory element may store, and the processing module, module, processing circuit, processing circuitry and/or processing unit executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated in one or more of the Figures. Such a memory device or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of method steps illustrating the performance of specified functions and relationships thereof. The boundaries and sequence of these functional building blocks and method steps have been arbitrarily defined herein for convenience of description. Alternate boundaries and sequences can be defined so long as the specified functions and relationships are appropriately performed. Any such alternate boundaries or sequences are thus within the scope and spirit of the claims. Further, the boundaries of these functional building blocks have been arbitrarily defined for convenience of description. Alternate boundaries could be defined as long as the certain significant functions are appropriately performed. Similarly, flow diagram blocks may also have been arbitrarily defined herein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence could have been defined otherwise and still perform the certain significant functionality. Such alternate definitions of both functional building blocks and flow diagram blocks and sequences are thus within the scope and spirit of the claims. One of average skill in the art will also recognize that the functional building blocks, and other illustrative blocks, modules and components herein, can be implemented as illustrated or by discrete components, application specific integrated circuits, processors executing appropriate software and the like or any combination thereof.

In addition, a flow diagram may include a “start” and/or “continue” indication. The “start” and “continue” indications reflect that the steps presented can optionally be incorporated in or otherwise used in conjunction with one or more other routines. In addition, a flow diagram may include an “end” and/or “continue” indication. The “end” and/or “continue” indications reflect that the steps presented can end as described and shown or optionally be incorporated in or otherwise used in conjunction with one or more other routines. In this context, “start” indicates the beginning of the first step presented and may be preceded by other activities not specifically shown. Further, the “continue” indication reflects that the steps presented may be performed multiple times and/or may be succeeded by other activities not specifically shown. Further, while a flow diagram indicates a particular ordering of steps, other orderings are likewise possible provided that the principles of causality are maintained.

The one or more embodiments are used herein to illustrate one or more aspects, one or more features, one or more concepts, and/or one or more examples. A physical embodiment of an apparatus, an article of manufacture, a machine, and/or of a process may include one or more of the aspects, features, concepts, examples, etc. described with reference to one or more of the embodiments discussed herein. Further, from figure to figure, the embodiments may incorporate the same or similarly named functions, steps, modules, etc. that may use the same or different reference numbers and, as such, the functions, steps, modules, etc. may be the same or similar functions, steps, modules, etc. or different ones.

Unless specifically stated to the contra, signals to, from, and/or between elements in a figure of any of the figures presented herein may be analog or digital, continuous time or discrete time, and single-ended or differential. For instance, if a signal path is shown as a single-ended path, it also represents a differential signal path. Similarly, if a signal path is shown as a differential path, it also represents a single-ended signal path. While one or more particular architectures are described herein, other architectures can likewise be implemented that use one or more data buses not expressly shown, direct connectivity between elements, and/or indirect coupling between other elements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of the embodiments. A module implements one or more functions via a device such as a processor or other processing device or other hardware that may include or operate in association with a memory that stores operational instructions. A module may operate independently and/or in conjunction with software and/or firmware. As also used herein, a module may contain one or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes one or more memory elements. A memory element may be a separate memory device, multiple memory devices, or a set of memory locations within a memory device. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. The memory device may be in a form a solid-state memory, a hard drive memory, cloud memory, thumb drive, server memory, computing device memory, and/or other physical medium for storing digital information.

While particular combinations of various functions and features of the one or more embodiments have been expressly described herein, other combinations of these features and functions are likewise possible. The present disclosure is not limited by the particular examples disclosed herein and expressly incorporates these other combinations.

Claims

1. A method comprises: receiving, by a storage network processing module, a write request for an encoded data slice of a set of encoded data slices, wherein data is dispersed error encoded in accordance with dispersed error encoding parameters to produce a set of encoded data slices;generating, by the storage network processing module, parity data for the encoded data slice;sending, by the storage network processing module, the encoded data slice to a first storage unit of a set of storage units; andsending, by the storage network processing module, the parity data for the encoded data slice to a second storage unit of the set of storage units.

2. The method of claim 1, further comprises: receiving, by a storage network processing module, a write update request for the encoded data slice;updating, by the storage network processing module, the encoded data slice to produce an updated encoded data slice;generating, by the storage network processing module, parity data for the updated encoded data slice; andsending, by the storage network processing module, the parity data for the updated encoded data slice to a third storage unit of a set of storage units.

3. The method of claim 1, further comprises: receiving, by a storage network processing module, a write update request for the encoded data slice;updating, by the storage network processing module, the encoded data slice to produce an updated encoded data slice;generating, by the storage network processing module, delta parity data for the updated encoded data slice; andsending, by the storage network processing module, the parity data for the updated encoded data slice to a third storage unit of a set of storage units.

4. The method of claim 1, further comprising: receiving, by the storage network processing module, a write request for another encoded data slice of the set of encoded data slices;generating, by the storage network processing module, parity data for the another encoded data slice;transmitting, by the storage network processing module, the another encoded data slice to a third storage unit of the set of storage units; andtransmitting, by the storage network processing module, the parity data for the another encoded data slice to a fourth storage unit of the set of storage units.

5. The method of claim 1, further comprising: updating, by the storage network processing module, parity information of an encoded data slice of at least one other encoded data slice of the set of encoded data slices, wherein the updating is based on a corresponding one parity data to produce an encoded data slice that includes updated parity data.

6. The method of claim 1, wherein data is segmented into a plurality of data segments before the data is dispersed error encoded, wherein each data segment of the plurality of data segments is dispersed error encoded in accordance with dispersed error encoding parameters to produce a set of encoded data slices.

7. The method of claim 1, further comprises: receiving, by a storage network processing module, delta parity data for the encoded data slice;retrieving, by the storage network processing module, the parity data for the encoded data slice;generating, by the storage network processing module, based on the delta parity data and the parity data, updated parity data for the encoded data slice; andsending, by the storage network processing module, the parity data for the updated encoded data slice to a third storage unit of a set of storage units.

8. A method comprises: receiving, by a storage network processing module, a write request for an encoded data slice of a set of encoded data slices, wherein data is encoded in accordance with a dispersed storage error coding function to produce a set of encoded data slices;generating, by the storage network processing module, a parity slice for each encoded data slice of the set of encoded data slices to produce a plurality of parity slices;transmitting, by the storage network processing module, a write threshold number of encoded data slices of the set of encoded data slices to a first set of storage units; andtransmitting, by the storage network processing module, the plurality of parity slices to a second set of storage units.

9. The method of claim 8, further comprises: receiving, by the storage network processing module, a write update request for an encoded data slice of the set of encoded data slices;updating, by the storage network processing module, the encoded data slice to produce an updated encoded data slice;generating, by the storage network processing module, an parity slice for the updated encoded data slice; andsending, by the storage network processing module, the parity slice for the updated encoded data slice to a third set of storage units.

10. The method of claim 8, further comprises: receiving, by the storage network processing module, a write update request for an encoded data slice of the set of encoded data slices;updating, by the storage network processing module, the encoded data slice to produce an updated encoded data slice;generating, by the storage network processing module, a delta parity slice for the updated encoded data slice; and

11. The method of claim 8, further comprising: updating, by the storage network processing module, a parity slice of an associated encoded data slice of at least one other encoded data slice of the set of encoded data slices, wherein the updating is based on a corresponding one parity slice to produce an encoded data slice that includes updated parity data.

12. The method of claim 8, wherein data is segmented into a plurality of data segments before the data is dispersed error encoded, wherein each data segment of the plurality of data segments is dispersed error encoded in accordance with dispersed error encoding parameters to produce a set of encoded data slices.

13. The method of claim 8, further comprises: receiving, by a storage network processing module, delta parity data for an encoded data slice of the set of encoded data slices;retrieving, by the storage network processing module, the parity slice associated with the encoded data slice;generating, by the storage network processing module, based on the delta parity data and the parity slice, an updated parity slice for the encoded data slice; andsending, by the storage network processing module, the updated parity slice for the updated encoded data slice to a third set of storage units.

14. A method comprises: receiving, by a storage unit of a storage network, a write request for an parity slice of a plurality of parity slices, wherein data is encoded in accordance with a dispersed storage error coding function to produce a set of encoded data slices, wherein a plurality of parity slices are generated from the set of encoded data slices;storing, by the storage unit, the parity slice in a memory unit associated with the storage unit;receiving, by the storage unit, an updated parity slice for the encoded data parity slice; andreplacing, by the storage unit, the parity slice in the memory unit with the updated encoded data parity slice.

15. The method of claim 14, further comprises: receiving, by the storage unit, delta parity data for the parity sliceretrieving, by the storage unit, the parity slice;generating, by the storage unit, based on the delta parity data and the parity slice, an updated parity slice for the encoded data slice; andstoring, by the storage unit, the parity slice in another memory unit associated with the storage unit.

16. The method of claim 14, further comprises: receiving, by the storage unit, a write update request for an encoded data slice associated with the parity slice;generating, by the storage unit, an updated parity slice for the updated encoded data slice; andstoring, by the storage unit, the updated parity slice in another memory unit associated with the storage unit.

17. The method of claim 14, further comprises: receiving, by the storage unit, delta parity data for the parity slice;retrieving the parity slice; generating, by the storage unit, based on the delta parity data and the parity slice, an updated parity slice; andstoring, by the storage unit, the updated parity slice in another memory unit associated with the storage unit.

18. The method of claim 14, wherein data is segmented into a plurality of data segments before the data is dispersed error encoded, wherein each data segment of the plurality of data segments is dispersed error encoded in accordance with dispersed error encoding parameters to produce a set of encoded data slices.