Ensuring user privacy in a publisher-subscriber communications environment. Storing, by a user-identifier mapping server, a user-identity database mapping user identity to subscriber-anonymized user identifier and subscriber identifier for users of said plurality of subscribers. Receiving, by the user-identifier mapping server, an information-request message from a subscriber, the information-request message concerning a notification message from a publisher, the notification message including an anonymized username of a first user of the publisher and wherein the username of the first user is anonymized using the one-way anonymization function. Upon receiving, by the user-identifier mapping server, the information-request message, determining from the user-identity database whether the first user is a user of the subscriber and transmitting a response message to subscriber indicating whether the first user is a user of the subscriber.

Cryptographic systems, methods and communication network comprising thereof are disclosed, including numerous industry applications. Embodiments of the present invention can generate and regenerate the same symmetric key. The cryptographic systems and methods include a key generator configured to use two or more inputs to reproducibly generate the symmetric key and a cryptographic engine configured to use the symmetric key for encrypting and decrypting data.

A method may include publishing metadata to a blockchain, the metadata describing a training task associated with a global machine-learning model and computational resource requirements for performing the training task. The method may include receiving a request to participate in training the global machine-learning model from one or more clients based on a relevance of a respective local dataset of each of the clients to the training task and a suitability of the clients to the computational resource requirements for performing the training task. The method may include obtaining local model updates in which each respective local model corresponds to a respective client and each respective local model update is generated based on training the global machine-learning model with each of the local datasets. The method may include aggregating the plurality of local model updates and generating an updated global machine-learning model based on the aggregated local model update.

A method may include publishing metadata to a blockchain, the metadata describing a training task associated with a global machine-learning model and computational resource requirements for performing the training task. The method may include receiving a request to participate in training the global machine-learning model from one or more clients based on a relevance of a respective local dataset of each of the clients to the training task and a suitability of the clients to the computational resource requirements for performing the training task. The method may include obtaining local model updates in which each respective local model corresponds to a respective client and each respective local model update is generated based on training the global machine-learning model with each of the local datasets. The method may include aggregating the plurality of local model updates and generating an updated global machine-learning model based on the aggregated local model update.

Some embodiments enable distributing data (e.g., recorded video, photographs, recorded audio, etc.) to a plurality of users in a manner which preserves the privacy of the respective users. Some embodiments leverage homomorphic encryption and proxy re-encryption techniques to manipulate the respective data so that selected portions of it are revealed according to an identity of the user currently accessing the respective data.

A method includes collecting a plurality of masked datasets. In certain embodiments, each masked dataset is associated with a one-time pad. The method can further include aggregating the plurality of masked datasets such that the one-time pads cancel each other to create an unmasked aggregated dataset.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating decentralized, privacy-preserving cryptographic proofs of liabilities in connection with immutable databases. In particular, in one or more embodiments, the disclosed systems enable an entity to transparently and accurately report its total amount of liabilities, obligations or other data related to fungible negative reports without exposing any user data or sensitive system data (e.g., the liabilities structure). Furthermore, the disclosed systems can generate a cryptographic proof of liability that allows individual users to independently verify that their committed liability is included in a reported total liability.

The present disclosure involves systems, software, and computer implemented methods for a efficient distributed secret shuffle protocol for encrypted database entries using independent shufflers. Each of multiple data providers provides an encrypted secret input value. A set of shuffling clients, independent of the data providers, participate with a service provider in a secret shuffling of the encrypted secret input values. The protocol includes generation and exchange of random numbers, random permutations and different blinding values. A last protocol step includes using homomorphism, for each client, to perform computations on intermediate encrypted data to homomorphically remove a first blinding value and a second blinding value, to generate a rerandomized encrypted secret input value. As a result, the rerandomized encrypted secret input values are generated in an order that is unmapped to an order of receipt, at the service provider, of the encrypted secret input values.

A producer may supply amounts x.sub.i of a good to a plurality of consumers C.sub.i in a series of transactions and be subject to a mass balancing verification protocol after every K transactions. A producer platform may compute K random shares (r.sub.1 through r.sub.K) of a random value r, publish blinded amounts t.sub.i representing x.sub.i+r.sub.i to a secure, distributed transaction ledger, and transmit an encrypted r.sub.i to consumer C.sub.i using an asymmetric cryptosystem. A consumer platform may receive and decrypt r.sub.i (while the consumer Ci actually receives an amount {circumflex over (x)}.sub.i of the good from the producer), compute {circumflex over (x)}.sub.i+r.sub.i and generate a fraud alert signal if it differs from the published t.sub.i. The consumer platform may also transmit an encrypted rolling sum value to a next consumer C.sub.i+1. A verifier platform may, after K transactions, execute the mass balance verification protocol to determine a total amount of the good that the producer had collectively supplied to the consumers C.sub.i. The verifier platform may also generate a fraud alert signal when appropriate based on the total amount and a maximum allowed amount.

An embodiment of the present invention is directed to providing third party certificate management for native mobile apps or IoT apps. An embodiment of the present invention is directed to performing vendor certificate pinning for trusted communications in native mobile apps without having to control vendor certificate lifecycle management. With an embodiment of the present invention, downloaded certificates may be protected by encryption, anti-tampering protection, etc.