Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

To revoke a digital certificate, activation of the digital certificate is blocked by withholding an activation code from the certificate user. The certificates are generated by a plurality of entities in a robust process that preserves user privacy (e.g. anonymity) even in case of collusion of some of the entities. The process is suitable for connected vehicles, e.g. as an improvement for Security Credential Management System (SCMS).

Disclosed herein are computer-implemented method, system, and computer-program product (computer-readable storage medium) embodiments for benchmarking with statistics in a way that reduces leakage, preserving privacy of participants and secrecy of participant data. An embodiment includes receiving a plurality of encrypted values and computing a composite statistic corresponding to at least a subset of the plurality of encrypted values. An embodiment may further include outputting the at least one composite statistic. The composite statistic may be calculated to be distinct from any encrypted value of the plurality of encrypted values, thereby preserving privacy. Further embodiments may also include generating a comparison between the composite statistic and a given encrypted value of the plurality of encrypted values, as well as outputting a result of the comparison. In some embodiments, encrypted values may be encrypted using at least one encryption key, for example, according to a homomorphic or semi-homomorphic encryption scheme.

Techniques described herein include utilizing a mobile device as a proxy receiver and/or transmitter for a vehicle in a V2X network. In some embodiments, the mobile device associated mobile device capabilities may be configured to obtain vehicle capabilities and store such data in memory at the mobile device. The mobile device may obtain any suitable combination of a reception credential and one or more transmission credentials. In some embodiments, the one or more transmission credentials may be generated by a credential authority based at least in part on determining that the vehicle capabilities and mobile device capabilities indicate that the sensor(s) and/or processing resources of the vehicle and/or mobile device meet transmission requirement thresholds for the network. The mobile device may subsequently transmit any suitable data message on behalf of the vehicle using at least one of the transmission credentials.

Evaluating risk of sensitive data associated with a target data set includes a computer system receiving a pattern that defines sensitive data and a selection of a data set as the target data set for evaluating. The system determines portions of the target data set from which to select sample data sets and determines, responsive to a confidence limit and sizes of the respective portions of the target data, a size of a sample data set for each respective target data set portion. The system randomly samples the target data set portions to provide sample data sets of the determined sample data set sizes and determines whether there is an occurrence of the sensitive data in each sample data set by searching for the pattern in the sample data sets. The system determines a proportion of the sample data sets that have the occurrence of the sensitive data.

Disclosed is a method that includes training, at a client, a part of a deep learning network up to a split layer of the client. Based on an output of the split layer, the method includes completing, at a server, training of the deep learning network by forward propagating the output received at a split layer of the server to a last layer of the server. The server calculates a weighted loss function for the client at the last layer and stores the calculated loss function. After each respective client of a plurality of clients has a respective loss function stored, the server averages the plurality of respective weighted client loss functions and back propagates gradients based on the average loss value from the last layer of the server to the split layer of the server and transmits just the server split layer gradients to the respective clients.

A third-party intermediary manages a protocol that prohibits the third-party intermediary from substantively accessing data content that, at least in part, underlies received protocol-compliant requests. By one approach, these teachings provide for preventing substantive access to data information that is included within the protocol-compliant request as one or more functions of data, parts of which data may be in tokenized or untokenized form, wherein the values of the functions are generated using secrets, at least one of which is unavailable to the third-party intermediary. By one approach, tokens comprised of data in tokenized form are generated using secrets, at least one of which is unavailable to the third-party intermediary.

Described herein are a system and techniques for enabling biometric authentication without exposing the authorizing entity to sensitive information. In some embodiments, the system receives a biometric template from a user device which is encrypted using a public key associated with the system. The encrypted biometric template is then provided to a second entity along with a biometric identifier. Upon receiving a request to complete a transaction that includes the biometric identifier and a second biometric template, the second entity may encrypt the second biometric template using the same public key associated with the system and perform a comparison between the two encrypted biometric templates. The resulting match result data file is already encrypted and can be provided to the system to determine an extent to which the two biometric templates match.

A computer-implemented method includes: determining assets held by a remitter, the assets to be spent in a remittance transaction between the remitter and one or more payees, in which each asset corresponds to a respective asset identifier, a respective asset amount, and a respective asset commitment value; determining a remitter pseudo public key and a remitter pseudo private key; determining a cover party pseudo public key, in which the cover party pseudo public key is obtained based on asset commitment values of assets held by the cover party; and generating a linkable ring signature for the remittance transaction.

Systems and methods for computing data privacy-utility tradeoff is disclosed. Large data hubs like data marketplace are a source of data that may be of utility to data buyers. However, output data provided to data sellers is required to meet the privacy requirements of data sellers and at the same time maintain a level of utility to data buyers. Conventionally known methods of achieving data privacy tend to suppress components of data that may result in reduced utility of the data. Systems and methods of the present disclosure compute this tradeoff to establish need for data transformation, if any, before data is shared with data sellers.