A settlement processing device, which is an example of an information processing device, includes an acquisition unit, a verification unit, and a providing unit. The acquisition unit acquires, from a user terminal used by a user who is a request source for a service, proof information that is for proving, by zero knowledge proof, that a user is an identity verified user, and that is generated by using secret information that only the identity verified user is allowed to know. A verification unit executes a verification process of proof information acquired by an acquisition unit by using encrypted information of identity verification information used in an identity verification process of the identity verified user managed in a block chain system, where the encrypted information is encrypted using secret information. The providing unit executes a process for providing a service to a user who is a request source for a service on condition that the user is proved to be an identity verified user as a result of the verification process by the verification unit.

Methods, systems, and apparatus, including a method for determining network measurements. In some aspects, a method includes receiving, by a first aggregation server and from each of multiple client devices, encrypted impression data. A second aggregation server receives, from each of at least a portion of the multiple client devices, encrypted conversion data. The first aggregation server and the second aggregation server perform a multi-party computation process to generate chronological sequences of encrypted impression data and encrypted conversion data and to decrypt the encrypted impression data and the encrypted conversion data.

A method for privacy-preserving inventory matching may include: (1) receiving a plurality of axe submissions; (2) arranging the parties into data structures based on a direction in the party's axe submission; (3) sending each party's commitment to the other party; (4) receiving, from each party, output secret-shares of an arithmetized comparison circuit; (5) verifying that the output secret-shares of the arithmetized comparison circuit received from the parties match commitments to the output secret-shares sent by the respective opposite party; (6) identifying a minimal party based on the outputs of the arithmetized comparison circuit; (7) generating and sending a proof of the minimal party identification to the minimal party; (8) receiving a minimal quantity integer from the minimal party; (9) revealing the minimal quantity integer to the first party and the second party; and (10) executing the trade for the minimal quantity integer.

In an example, a first metadata tag and a second metadata tag are added to first Personally Identifiable Information (PII) of a first user handled by a first application. The first PII is to be part of call home data captured from a hosting system. The first metadata tag may be indicative of security rules to be complied with for the first application and the second metadata tag may be indicative of security rules to be complied with for the first user. The first PII, the first metadata tag, and the second metadata tag may be protected and transmitted to a data processing center. The transmission may be in response to a determination to transmit the call home data.

Techniques regarding privacy preservation in a federated learning environment are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a plurality of machine learning components that can execute a machine learning algorithm to generate a plurality of model parameters. The computer executable components can also comprise an aggregator component that can synthesize a machine learning model based on an aggregate of the plurality of model parameters. The aggregator component can communicate with the plurality of machine learning components via a data privacy scheme that comprises a privacy process and a homomorphic encryption process in a federated learning environment.

A computer-implemented method is for providing processed data. In an embodiment, the method includes receiving, by a first encryption entity, first plaintext data including a matrix of numbers; determining, by the first encryption entity, an encryption key including an integer matrix; homomorphically encrypting, by the first encryption entity, the first plaintext data based on a matrix multiplication of the first plaintext data and the encryption key, to generate first encrypted data; sending, by the first encryption entity, the first encrypted data to a processing entity; receiving, by a decryption entity, encrypted processed data from the processing entity, the encrypted processed data being based on the first encrypted data; decrypting, by the decryption entity, the encrypted processed data based on a matrix multiplication of the processed data and an inverse of the encryption key, to generate processed data; and providing, by the decryption entity, the processed data.

Systems and methods relating to providing identity privacy over a trusted or untrusted non-3GPP access network in a wireless communication system are disclosed. In some embodiments, a method of operation of a wireless device comprises sending a message to a gateway (ePDG, N3IWF or TWAG) where the message comprises an anonymous user identity; receiving a request for obfuscating the user identity wherein the request comprises a server certificate; and validating the server certificate and sending a response message back to the gateway, comprising the user identity obfuscated by a public key associated with the server certificate. Similar methods are provided on the gateway side and AAA server side. In this manner, the user identity is protected when establishing the connection to the core network and protects against a man in the middle attack.

Embodiments disclosed herein are related to computing systems and methods for generating one or more pseudonymous names for use by a Decentralized Identifier (DID) owner when interacting with third party entities. An indication is received from a DID owner who is associated with a DID. The indication indicates that the DID owner desires to interact with various third party entities. A list is generated of pseudonymous names that are to be used in place of the DID as the DID owner interacts with the one or more third party entities. A selection is received for a specific one of the generated pseudonymous names. The selected specific pseudonymous name is bound to the DID so that the selected specific pseudonymous name is used during the interaction.

In one embodiment, a network assurance service maintains a first set of telemetry data from the network anonymized using a first key regarding a plurality of network entities in a monitored network. The service receives a key rotation notification indicative of a key changeover from the first key to a second key for anonymization of a second set of telemetry data from the network. The service forms, during a key rotation time period associated with the key changeover, a mapped dataset by converting anonymized tokens in the second set of telemetry data into anonymized tokens in the first set of telemetry data. The service augments, during the key rotation time period, the first set of telemetry data with the mapped dataset. The service assesses, during the time period, performance of the network by applying a machine learning-based model to the first set of telemetry data augmented with the mapped dataset.

The present invention addresses the issue of secure and trusted Internet of Things (IoT) blockchain networks by adopting the emerging blockchain technologies. The present invention proposes a new hybrid blockchain technology to address the trusted IoT issues such as trustless communications and decentralized applications. Besides, the present invention also disclose that the pseudonymous authentication technique can use a puzzle-solving computation to enable trustless communications for the IoT and provide the capabilities of near real-time transactions.