Aspects of the present invention relate generally to videoconferencing and, more particularly, to securing data presented during videoconferencing.
One type of electronic meeting is videoconferencing, which is the holding of a conference among people at remote locations by way of transmitted audio and video signals. Videoconferencing typically involves each user being connected to a videoconferencing server via their computing device. Video of the videoconference is displayed by each user's computing device, e.g., in an interface of the videoconference program. Audio of the videoconference is output by speakers included in or connected to each user's computing device. In some instances, a user computing device may include a camera for capturing video of the user and a microphone for capturing audio of the user, which is combined into the stream of the videoconference that is seen and heard by other users.
A common feature of videoconferencing is to share one's screen with other members of the videoconference. Using this feature, a user may show a document displayed in a window on their computing device to other users in the videoconference. The screenshare appears in the other users' videoconference interface.
In a first aspect of the invention, there is a computer-implemented method including: determining, by a videoconference server, a level of tolerated risk for a videoconference between a presenter and an attendee; obtaining, by the videoconference server, sensor data from at least one sensor at a location where a user device of the attendee displays the videoconference; generating, by the videoconference server, a current risk score based on the sensor data; determining, by the videoconference server, the current risk score exceeds the level of tolerated risk; and presenting, by the videoconference server and in response to the determining the current risk score exceeds the level of tolerated risk, an alert to the presenter of the videoconference.
In another aspect of the invention, there is a computer program product including one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media. The program instructions are executable to cause a videoconference server to: determine a level of tolerated risk for a videoconference between a presenter and an attendee; obtain sensor data from at least one sensor at a location where a user device of the attendee displays the videoconference; generate a current risk score based on the sensor data, wherein the current risk score is a probability that another person is in the location where the user device of the attendee displays the videoconference; determine the current risk score exceeds the level of tolerated risk; and in response to the determining the current risk score exceeds the level of tolerated risk, present an alert to the presenter of the videoconference.
In another aspect of the invention, there is system including a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media. The program instructions are executable to: determine a level of tolerated risk for a videoconference between a presenter and an attendee; obtain sensor data from at least one sensor at a location where a user device of the attendee displays the videoconference; generate a current risk score based on the sensor data, wherein the current risk score is a probability that another person is in the location where the user device of the attendee displays the videoconference; determine the current risk score exceeds the level of tolerated risk; and in response to the determining the current risk score exceeds the level of tolerated risk concurrently with confidential data being displayed in the videoconference, present an alert to the presenter of the videoconference
Aspects of the present invention are described in the detailed description which follows, in reference to the noted plurality of drawings by way of non-limiting examples of exemplary embodiments of the present invention.
Aspects of the present invention relate generally to videoconferencing and, more particularly, to securing data presented during videoconferencing. Implementations of the invention leverage sensor data and artificial intelligence (AI) to detect when confidential data presented during a videoconference is susceptible to being viewed by an unauthorized person. In response to this detecting, embodiments cause the videoconference system to output an alert to one or both of the videoconference presenter and the videoconference attendee. In this manner, implementations of the invention are usable to provide security measures for confidential data that is displayed during a videoconference.
Organizations constantly struggle to secure their critical data within their own premises, as well as outside their premises, for multiple security compliance reasons. A data security breach could happen either because of human error or with malicious intent.
An organization's endpoint devices such as laptops, mobile tablets, smartphones, and handheld gadgets are the new way of working due to ease of mobility and accessibility; however, managing the security of these devices is difficult. Users often utilize these devices to access the organization's data by connecting to unsecured networks such as public networks and private networks that are outside the control of the organization. This behavior increases the likelihood of a data breach because of the unsecure network connections.
To reduce the likelihood of a data breach, organizations sometimes provide their users with secure data communication through a virtual private network (VPN) using a firewall with which data is encrypted when at rest and in motion. There are also solutions available that provide secure cloud or on-premises storage of backups of data saved on endpoint devices. Using these solutions, a user that loses their endpoint device can have their data restored from the backup.
Another security level for endpoint devices is achieved using security monitoring and analysis, which captures data on the overall state of a system, including endpoint devices and connectivity traffic. This data is analyzed to detect possible security violations or potential system threats based on anomalous behavior.
Multi-factor authentication is yet another way of securing data access on endpoint devices. But it is still critical to ensure that endpoints devices are secured from possible data breach, tampering, and manipulation, which could result in financial loss to an organization.
Videoconferencing is near-ubiquitous and has become a normal way to communicate in both personal and professional settings. In the professional setting, videoconferencing is a normal way to communicate with co-workers and other collaborators. In some situations, confidential data is shared during the videoconference. For example, a user may screenshare a document during the videoconference, and the document being shared may include confidential data that is intended to be seen only by the attendees of the videoconference. However, there are situations when a person that is not an attendee of the videoconference can visually see the display of the videoconference as it is displayed by the device of an attendee. For example, a videoconference attendee may be seated and participating in a videoconference using their laptop, and a non-attendee may walk near where the attendee is seated and be able to see the display of the laptop. In this situation, the non-attendee is able to see the visual content of the videoconference, which might include confidential data that the non-attendee is not authorized to see. This is a form of data breach that is present is current videoconferencing systems.
Implementations of the invention address these problems by providing enhancements to videoconferencing systems that detect when an unauthorized person is capable of seeing confidential data presented during a videoconference and, in response to the detecting, cause the videoconference system to output an alert to one or both of the videoconference presenter and the videoconference attendee. In embodiments, the system detects and connects with Internet of Things (IoT) sensors or security devices at the attendee's location and utilizes the output of these sensors or security devices to predict when another person is in a position to see the attendee's computer display that is displaying content of the videoconference. In embodiments, the system includes an artificial intelligence component, such as one or more machine learning models, that receive the sensor data as input and that output a scored probability that another user can see the videoconference on the attendee's device. In this manner, implementations of the invention address the above-described problem of data breach during videoconferencing by collecting specialized sensor data, using the sensor data with artificial intelligence to predict a scored probability of a data breach, and generating an alert when the scored probability exceeds a threshold.
As will be understood from the description herein, implementations of the invention may include a computer implemented method comprising: identifying, by a computer, a Level of Tolerated Risk (LTR) associated with presentation content; determining, by the computer, using an AI model, that a present level of risk associated with a presentation environment exceeds the LTR; and initiating, by the computer, a Cautionary Action (CA) in response to the determining. The LTR may be based on the attributes of the presentation material (e.g., extracted from content metadata or provided by a content provider). The AI model may be trained to recognize conditions relevant to the presentation environment (e.g., as indicated by IoT sensors associated with the presentation environment). The AI model may consider presentation environment conditions indicated by recognized facial expressions of a relevant presenter. The CA may provide a warning to a presenter regarding the presence in the presentation environment of at least one of an unwanted audience member, an unwanted recording device, and unwanted presentation recording software. The CA may provide a visual indication to the presenter (e.g., a color-coded screen overlay, etc.). The computer may be in communication with the IoT sensors located proximate to the presentation environment.
As described herein, a videoconference presenter may present confidential data to the videoconference attendees, and this data may be at risk of a breach due to other persons being able to see the attendees' display of the videoconference. Implementations of the invention improve a presenter's experience in the video conference by providing security measures to help reduce the likelihood of such a breach. Implementations may be used in addition to existing multi-factor authentication mechanisms by which an attendee confirms their identify before joining the videoconference.
Embodiments provide a mechanism that will detect if a third-party person is in the attendee's endpoint device field of view. Implementations may use one or more different types of sensors to detect a third-party person within the vicinity of the attendee's endpoint device.
Implementations can be used to enhance videoconference software to detect existing IoT sensors or security devices in the vicinity of an attendee's endpoint device. Examples of such sensors include but are not limited to cameras, ultrasonic sensors, infrared sensors, light detection and ranging (LIDAR) sensors, Li-Fi sensors, microphones, and carbon content detection sensors. In embodiments, when an attendee endpoint device is first connecting to a videoconference, a system causes the attendee endpoint device to discover and connect to such sensors that are within a same room as the attendee endpoint device. In embodiments, the system leverages the output of the connected sensors to detect when another person enters the room with the attendee during the videoconference, and to generate an alert to the attendee and/or the videoconference presenter based on the detecting the other person has entered the room.
Implementations may be used to enhance videoconferencing systems by configuring the systems to learn and recognize user expressions that are associated with another person entering a room with an attendee of the videoconference. This may be performed using an AI component such as a machine learning model that is trained to recognize user expressions that are associated with another person entering a room. In this manner, the system may use the machine learning model in real time during a videoconference to detect when a person has entered a room with an attendee of the videoconference. In one example, the user expressions comprise facial expressions and the AI component comprises a convolutional neural network (CNN) trained for facial expression recognition.
Implementations may be used to prevent a third-party person from obtaining an image of the attendee's endpoint device while the endpoint device is displaying confidential data during the videoconference. In embodiments, this is achieved using infrared blocking techniques.
In embodiments, the system determines a subset of IoT sensors to use for detecting the presence of an unauthorized person in the room with the videoconference attendee. In these embodiments, the system may enable communication with the determined subset of IoT sensors and disable communication with other ones of the IoT sensors that are not included in the subset.
In implementations, the system generates an alert to the videoconference presenter when a determined risk score exceeds a predefined threshold. The alert can be in the form of a hint that is presented to the videoconference presenter in their interface of the videoconference. In response to receiving the hint, the videoconference presenter may take action to stop displaying the confidential material in the videoconference. In one example, the videoconference presenter may stop a screen share of a confidential document. In another example, the videoconference presenter may disconnect the attendee from the videoconference. In one exemplary implementation, the hint comprises one of plural different visualizations that signify different levels of concern, such as a green icon signifying a low level of concern of a data breach, a yellow icon signifying a medium level of concern, and a red icon signifying a high level of concern.
Implementations as described herein enable a videoconferencing system to identify a confidentiality level requirement for a videoconference as set by the presenter. The confidentiality level requirement may be one of high, medium, and low, for example. The confidentiality level requirement may be identified using machine learning techniques that determine the confidentiality from the content of the videoconference before presentation in the videoconference. The confidentiality level requirement may be identified using a tagging method in which a frame in the video or slide in the presentation is tagged as confidential.
Implementations as described herein enable a videoconferencing system to provide a presenter with a view through which the presenter can set required preferences before starting or setting up the videoconference. In this way, the videoconference attendees are made aware of and understand prerequisites for attending the videoconference. The prerequisites may include for example: each attendee should be alone in the field of view of their camera while confidential data is displayed; attendees should not screenshot an interface of the videoconference on the attendee's endpoint device; and attendees should not save content of the videoconference on the attendee's endpoint device using background recording software.
Implementations as described herein enable a videoconferencing system to detect and connect to existing IoT sensors in an attendee's location (e.g., room) using application programming interfaces (APIs) of the IoT sensors. In embodiments, the system creates categories of sensors based on the output data streams the sensors provide. In embodiments, the system processes data steams coming from the IoT sensors, such as a motion detector in the room, a sound detector trained for human identification, ultrasonic sensors, carbon content-based detectors, and Li-Fi based detectors that can identify human presence in the same room as the attendee endpoint device.
Implementations as described herein enhance a videoconferencing system by helping an attendee's endpoint device connect with IoT sensors automatically when the videoconference begins. In embodiments, the endpoint device is configured to identify to the videoconferencing system whether the endpoint device has ultrasonic and/or infrared sensors installed in the endpoint device itself, and to enable the videoconferencing system to use these sensors during the videoconference. In embodiments, the endpoint device is configured to identify to the videoconferencing system whether the endpoint device has some form of background recording and/or screenshot capability installed, and to enable the videoconferencing system to disable these functions on the endpoint device during the videoconference. In this manner, implementations as described herein enable a videoconferencing system to collaborate with existing hardware sensors and software capability on the attendee's endpoint device, and to collect information from these sensors and software for the purpose of maintaining the confidentiality of data presented in the videoconference.
Implementations as described herein enable a videoconferencing system to check whether the attendee's endpoint device has permission to access the IoT sensors in the room, to enable or disable the connection to certain ones of the IoT sensors during the videoconference, and to collect data from the IoT sensors for analysis. In embodiments, the system uses a machine learning model to learn facial expressions of the attendee that indicate another person is on the room. In this manner, the system may use the trained machine learning model in real time, with the attendee's facial expressions during the videoconference, to predict whether another person is in the room with the attendee.
Implementations as described herein enable a videoconferencing system to detect which IoT sensor data is relevant for identifying anomalies based on the defined confidentiality of the videoconference and analysis of the IoT sensor data. In embodiments and based on this relevancy determination, the system may selectively enable or disable an IoT sensor data stream during the videoconference.
Implementations as described herein enable a videoconferencing system to provide a hint to the videoconference presenter when an anomaly is detected during the videoconference. In response, the videoconference presenter can communicate with the attendee to indicate that the attendee's endpoint device does not meet the confidentiality criteria for attending the videoconference.
Implementations as described herein enable a videoconferencing system to capture environment anomalies and change a color of a visual indicator displayed in the videoconference based on a level of security. In this manner, by using a visual indicator, the presenter may be notified of the anomaly even if their audio is on mute.
It should be understood that, to the extent implementations of the invention collect, store, or employ personal information provided by, or obtained from, individuals, such information shall be used in accordance with all applicable laws concerning protection of personal information. Additionally, the collection, storage, and use of such information may be subject to consent of the individual to such activity, for example, through “opt-in” or “opt-out” processes as may be appropriate for the situation and type of information. Storage and use of personal information may be in an appropriately secure manner reflective of the type of information, for example, through various encryption and anonymization techniques for particularly sensitive information.
The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium or media, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions may be provided to a processor of a computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.
Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).
Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.
Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.
Referring now to
In cloud computing node 10 there is a computer system/server 12, which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server 12 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like.
Computer system/server 12 may be described in the general context of computer system executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system/server 12 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
As shown in
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus.
Computer system/server 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server 12, and it includes both volatile and non-volatile media, removable and non-removable media.
System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and/or cache memory 32. Computer system/server 12 may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system 34 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus 18 by one or more data media interfaces. As will be further depicted and described below, memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
Program/utility 40, having a set (at least one) of program modules 42, may be stored in memory 28 by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules 42 generally carry out the functions and/or methodologies of embodiments of the invention as described herein.
Computer system/server 12 may also communicate with one or more external devices 14 such as a keyboard, a pointing device, a display 24, etc.; one or more devices that enable a user to interact with computer system/server 12; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server 12 to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces 22. Still yet, computer system/server 12 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 20. As depicted, network adapter 20 communicates with the other components of computer system/server 12 via bus 18. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system/server 12. Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.
Referring now to
Referring now to
Hardware and software layer 60 includes hardware and software components. Examples of hardware components include: mainframes 61; RISC (Reduced Instruction Set Computer) architecture based servers 62; servers 63; blade servers 64; storage devices 65; and networks and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.
Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 71; virtual storage 72; virtual networks 73, including virtual private networks; virtual applications and operating systems 74; and virtual clients 75.
In one example, management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.
Workloads layer 90 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95; and videoconferencing security 96.
Implementations of the invention may include a computer system/server 12 of
In embodiments, the videoconference server 410 comprises one or more computing servers each comprising one or more elements of computer system 12 of
In embodiments, the videoconference server 410 comprises a conference application 425 which may comprise a software application such as a program/utility 40 of
In embodiments, the conference application 425 comprises a videoconferencing application that communicates with respective instances of the conference client application 420 on the user devices 405, 407 to provide videoconferencing services and functionality to the users of the user devices 405, 407. For example, the conference application 425 may receive audio and video signals from each of the user devices 405, 407 and generate a conference audio/video stream for output at each of the user devices 405, 407 so that the users of the user devices 405, 407 can participate in real-time videoconferencing. In embodiments, the conference application 425 is configured to permit a user of one of the user devices (e.g., 405) to share their screen with users of the other user devices (e.g., 407) during the real-time videoconferencing. For example, a first user may opt to share their screen showing a word-processing document, and the other users in the videoconference can see the screenshared portion of the word-processing document in a user interface of the conference client application 420 on their respective devices.
In embodiments, the conference application 425 is configured to permit a user of one of the user devices (e.g., 405) to be a presenter of the videoconference and another one of the user devices (e.g., 407) to be an attendee of the videoconference. For simplicity, only two user devices 405, 407 are shown in
With continued reference to
According to aspects of the invention, the security module 430 is configured to receive data from the sensors 435, detect when a person other than the attendee is in the location 440 based on the data from the sensors 435, and generate an alert to one or both the user devices 405, 407 based on the detecting the other person in the location 440.
In embodiments, the security module 430 is configured to determine when confidential data is displayed in the videoconference, and to generate the alert only when confidential data is being displayed and a person other than the attendee is in the location 440. In one example, the security module 430 determines when confidential data is being displayed in the videoconference based on user input (e.g., from the presenter using device 405). In another example, the security module 430 automatically determines when confidential data is being displayed in the videoconference by using an artificial intelligence component, such as natural language processing, object recognition in images, etc.
With continued reference to
Another example of a confidentiality requirement is the presenter providing user input to define certain portions of the content as confidential data. This may include, for example, tagging certain portions of a document (e.g., passages, paragraphs, pages, slides, etc.) as being confidential. Alternatively, to the presenter manually defining the confidential data, the security module 430 may automatically identify confidential data in the content of the videoconference. For example, the security module 430 may use artificial intelligence to automatically identify confidential data in the content of the videoconference. The artificial intelligence may include, for example, a text-based sensitivity technique or a video-subscript-based machine learning technique that identifies whether content contains confidential data. Additionally, or alternatively, the artificial intelligence may include natural language processing and keyword detection. For example, using keyword detection, the security module 430 may detect the word “confidential” on a slide of a document and may tag that slide as containing confidential data. In another example, using natural language processing the security module 430 may detect the phrase “this entire document is confidential” on one page of a multipage document and, based on this, may tag all pages of the document as confidential. These examples are non-limiting, and other techniques may be used to automatically identify confidential data in the content of the videoconference.
Another example of a confidentiality requirement is the presenter providing user input to define a level of tolerated risk for the videoconference. In one example, the level of tolerated risk is a numerical value that the presenter can input in a form field or by using a dial or slider in the videoconference interface on the presenter's user device (e.g., 405). In another example, the level of tolerated risk is one of a predefined set of levels such as low, medium, and high. In this example, each one of low, medium, and high has a different numerical value associated with it, and the numerical value of the one selected by the presenter (e.g., low, medium, and high) is set as the level of tolerated risk. As described later with respect to step 625 of
With continued reference to
With continued reference to
With continued reference to
In embodiments, when plural sensors are available (e.g., one or more IoT sensors and/or one or more sensors of the user device 407), the security module 430 selects a subset of the plural sensors and obtains sensor data from only the selected subset. In embodiments, the security module 430 selects the subset based on determining which of the sensors is most relevant in the current situation. The relative relevancy of the sensor data may be determined based on a quality of the sensor data, for example based on confidence levels of predictions made using the sensor data. In embodiments, the security module 430 disables the function of collecting sensor data from sensors that are not in the selected subset, thus reducing the usage of computing resources.
With continued reference to
In one exemplary implementation, the sensor 435 comprises a camera, the sensor data is image data of the camera that captures an expression of the attendee during the videoconference, and the risk score is generated based on the expression of the attendee. In this implementation, the security module 430 uses machine learning to learn facial expressions of the attendee that indicate another person is on the room. In this manner, the security module 430 may use the trained machine learning model in real time, with the attendee's facial expressions during the videoconference, to predict whether another person is in the room with the attendee.
With continued reference to
With continued reference to
In additional embodiments, the security module 430 may notify the presenter prior to the videoconference that the attendee's user device 407 does not have a certain capability. Upon receiving this information, the presenter might take action such as ask the attendee to add the capability to their user device 407 before beginning the videoconference.
In additional embodiments, the security module 430 may notify the presenter prior to the videoconference that the attendee is currently in a situation (e.g., travel) in which the attendee cannot satisfy one or more of the prerequisites. Upon receiving this information, the presenter might take action such as reschedule the videoconference to a later time when the attendee will be able to satisfy all of the prerequisites.
At step 605, the system determines confidentiality requirements of a videoconference that is presented by a presenter using user device 405 and attended by an attendee using user device 407. In embodiments, the security module 430 determines the confidentiality requirements of a videoconference based on at least one of user input and automated techniques. In embodiments, the user input comprises the videoconference presenter defining confidentiality requirements via their videoconference interface. This may include, for example, tagging certain portions of a document (e.g., passages, paragraphs, pages, slides, etc.) as being confidential. In embodiments, the automated techniques comprise conventional or later-developed automated techniques for identifying confidential data in a document. This may include, for example, natural language processing and keyword detection. The confidentiality requirements may also include a level of tolerated risk for the videoconference defined by the presenter.
At step 610, the system establishes a respective sensor cohort for each attendee of the videoconference. In embodiments, the client conference application 420 on the user device 407 discoveries and connects to sensors 435 in the location 440 of the user device 407. The discovery and connection may be performed using application programming interfaces (APIs) for each of the sensors. The senor cohort may additionally or alternatively include one or more sensors that are integrated with the user device 407. In embodiments, step 610 includes reporting sensor data from the sensor cohort to the security module 430.
At step 615, the system detects the presence of another person in the location with the attendee and user device 407. In embodiments, the security module 430 analyzes the sensor cohort data to derive a scored probability of another human presence for each attendee. In embodiments, for each attendee of the videoconference, the security module 430 receives data from the sensors 435 in real time and uses the data to determine whether another person is in the same location as the attendee. The presence detection may be performed by a presence detection module that is part of or communicates with the security module 430. The presence detection may be performed using one or more of: object detection (e.g., using a convolutional neural network) using data from a camera; motion sensing using data from an infrared sensor; light fidelity analysis using data from a Li-Fi sensor; ultrasonic human presence detection using data from an ultrasonic sensor; carbon content detection using data from a carbon content detection sensor; and LIDAR depth scan using data from a LIDAR sensor. In embodiments, the security module 430 determines a confidence score of the presence detection for each sensor used in the making the presence detection, wherein the confidence score represents a probability that another person is in the location with the attendee.
At step 620, the system generates an anomaly score (also called a current risk score) for each attendee. In embodiments, the security module 430 generates the anomaly score based on a confidence score(s) of the presence detection. In embodiments, when only one sensor 435 is used to make the presence detection at step 615, then the anomaly score is the confidence score of the presence detection derived from the data of that one sensor. In embodiments, when plural sensors 435 are used to make the presence detection at step 615, then the anomaly score is a function of the respective confidence scores of the presence detections derived from the data of the plural sensors. In one example, the function is a non-weighted average of the confidence scores of the presence detections of the plural sensors. In another example, the function is a weighted average of the confidence scores of the presence detections of the plural sensors, where the weights are configurable in settings of the security module 430. The generating the anomaly score may be performed by an anomaly scoring module that is part of or communicates with the security module 430. In embodiments, the anomaly scoring module reports the scoring for the videoconference.
At step 625, the system presents an alert to the presenter of the videoconference based on the anomaly score from step 620. In embodiments, the system augments the videoconference presenter's view with a visualization of a probability that each attendee is within the confines of the defined conference confidentiality requirements. In embodiments, the security module 430 causes the conference client application 420 to display one of plural predefined visual indications in the videoconference interface of the presenter's user device 405 based in the anomaly score from step 620. In one example, the plural predefined icons include a first icon, a second icon, and a third icon. In this example, the security module 430 causes the conference client application 420 to display the first icon when the anomaly score is less than a first threshold, to display the second icon when the anomaly score is between the first threshold and a second threshold, and to display the third icon when the anomaly score is greater than the second threshold. In this example, the first icon may be green and signify the risk level is low, the second icon may be yellow and signify the risk level is medium, and the third icon may be red and signify the risk level is high. The presenter may perform an action in the videoconference based on which icon is displayed in their videoconference interface.
Still referring to step 625, in one example the second threshold is set as the numerical value of the level of tolerated risk. In this example, the first threshold may be computed based on a predefined function, such as half the second threshold.
At step 705, the system determines a level of tolerated risk for a videoconference between a presenter and an attendee. At step 710, the system obtains sensor data from at least one sensor at a location where a user device of the attendee displays the videoconference. At step 715, the system generates a current risk score based on the sensor data. At step 720, the system determines the current risk score exceeds the level of tolerated risk. At step 725, in response to the determining the current risk score exceeds the level of tolerated risk at step 720, the system presents an alert to the presenter of the videoconference.
In embodiments, the alert comprises a visual indicator shown in an interface of the videoconference. In embodiments, the alert comprises one of plural visual indicators shown in an interface of the videoconference, the plural visual indicators comprising: a first visual indicator having a first color and signifying a first level of concern; a second visual indicator having a second color and signifying a second level of concern; and a third visual indicator having a third color and signifying a third level of concern, wherein the first color, the second color, and the third color are all different from one another.
In embodiments, a service provider could offer to perform the processes described herein. In this case, the service provider can create, maintain, deploy, support, etc., the computer infrastructure that performs the process steps of the invention for one or more customers. These customers may be, for example, a business that provides videoconferencing. In return, the service provider can receive payment from the customer(s) under a subscription and/or fee agreement and/or the service provider can receive payment from the sale of advertising content to one or more third parties.
In still additional embodiments, the invention provides a computer-implemented method, via a network. In this case, a computer infrastructure, such as computer system/server 12 (
The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
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