In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) an authentication system can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption of the encrypted feature vectors. Security of such privacy enable biometrics can be increased by implementing an assurance factor (e.g., liveness) to establish a submitted biometric has not been spoofed or faked.

Aspects of the technology described herein maintain the privacy of confidential information to be communicated to a user through a computing device. The technology keeps confidential information private by assessing the privacy context of the communication. The privacy context can be determined by determining a privacy level of the information to be communicated and the privacy level of the environment into which the information is to be communicated. The privacy context can be used to select an appropriate communication channel for the information. The privacy context can also be used to determine whether all available content is shared or just a portion of it.

A biometric scanner apparatus comprising a biometric sensor configured to scan at least a biological sample and receive a unique biometric pattern, a secret data extractor configured to receive the unique biometric pattern from the biometric sensor and generate an output comprising a sample-specific secret, and a sample identifier circuit communicatively connected to the secret data extractor wherein the sample identifier circuit is configured to produce at least an output comprising a secure proof of the sample-specific secret.

A processor may identify one or more transaction verification requests from one or more entities. The processor may convert each of the one or more transaction verification requests into respective hashed transaction verification requests. The processor may send, on one or more private, anonymous channels, the hashed transaction verifications to an orchestrator. The processor my decrypt the hashed transaction verifications with the orchestrator. The processor may determine whether information in each of the one or more transaction verification requests matches.

A distributed ledger-based system, method and apparatus for administering voting contests is disclosed. Potential voters send registration requests to a distributed ledger, and each computing node of the distributed ledger, executing a smart voting contract, registers the potential voters and issues each registered voter a cryptographic voting token. Each registered voter uses the cryptographic voting token to cast an electronic ballot, the electronic ballot comprising a distributed ledger-based voting transaction request. Each voting transaction request is received by each computing node of the distributed ledger and verified, and a distributed ledger-based, verified voting transaction is created and validated along with other verified voting transactions by each of the computing nodes. When the verified voting transactions are validated, a cryptographic block is created and added to a blockchain of the distributed ledger. After a voting contest has conclude, each of the computing nodes validates a final tally of voting tokens received by each candidate in the voting contest, and a final cryptographic block is published by the distributed ledger with the results. This application is related to NFT Origin Ethereum Address 0x7beaD10F8dE9fFd99A0E897840D6105BBBC1184f.

A method by one or more network devices executing one or more smart contracts stored in a blockchain for anonymizing faces appearing in digital media content. The method includes obtaining, for each of a plurality of users, a facial model associated with that user, obtaining digital media content digital media content, determining whether that detected face matches the face of any of the plurality of users based on applying one or more of the facial models associated with the plurality of users to that detected face, anonymizing that detected face to generate an anonymized face in response to a determination that that detected face matches the face of one of the plurality of users, and providing the anonymized face to the media platform.

In a vehicle-to-everything (V2X) technology environment, systems and methods are provided for extending the distribution of activation codes (ACs) in an Activation Codes for Pseudonym Certificates (ACPC) system, in a privacy-preserving manner, to a unicast mode of communication. In this unicast ACPC (uACPC), in some embodiments, the ACs are distributed by the back-end system via a unicast channel upon the receipt of the vehicle's direct request for its respective ACs. In some embodiments, uACPC can leverage edge computing architecture for low latency delivery of certificate revocation lists (CRLs) and higher availability for the distribution of ACs.

A secure inference over Deep Neural Networks (DNNs) using secure two-party computation to perform privacy-preserving machine learning. The secure inference uses a particular type of comparison that can be used as a building block for various layers in the DNN including, for example, ReLU activations and divisions. The comparison securely computes a Boolean share of a bit representing whether input value x is less than input value y, where x is held by a user of the DNN, and where y is held by a provider of the DNN. Each party computing system parses their input into leaf strings of multiple bits. This is much more efficient than if the leaf strings were individual bits. Accordingly, the secure inference described herein is more readily adapted for using in complex DNNs.

Techniques of storing and retrieving medical data and information are provided. Medical result information can be stored on a long-term data repository in a shared network, e.g., the Internet. Pseudonymized identifiers of patients can be used to retrieve such data and information.

Techniques described herein are directed to proxies configured to handle identity and access management for a web application. For instance, a first proxy receives requests to the application from a browser. The first proxy redirects the browser to an identity endpoint, which prompts the user to enter authentication credentials for the application. Upon successful authentication, the endpoint provides an access token for accessing web APIs to the first proxy. The first proxy provides the token to a second proxy, which stores the token. The second proxy receives anonymous API calls from the web application to the web APIs. When receiving an anonymous API call, the second proxy obtains the token and inserts it into an outgoing request to the API. Responsive to the API returning a message indicating that the token is invalid, the second proxy communicates with the first proxy to obtain a new token from the endpoint.