Embodiments of the present disclosure provide hierarchical, differential privacy enhancements to federated, machine learning. Local machine learning models may be generated and/or trained by data owners participating in the federated learning framework based on their respective data sets. Noise corresponding to and satisfying a first privacy loss requirement are introduced to the data owners' respective data sets, and noise corresponding to and satisfying a first privacy loss requirement are introduced to the local models generated and/or trained by the data owners. The data owners transmit model data corresponding to their respective local models to a coordinator, which in turn aggregates the data owners' model data. After introducing noise corresponding to and satisfying a third privacy loss requirement to the aggregated model data, the coordinator transmits the aggregated model data to the data owners to facilitate updating and/or re-training on their respective machine learning models.

This disclosure relates to a privacy preserving machine learning platform. In one aspect, a method includes receiving, from a client device and by a computing system of multiple multi-party computation (MPC) systems, a first request for user group identifiers that identify user groups to which to add a user. The first request includes a model identifier for a centroid model, first user profile data for a user profile of the user, and a threshold distance. For each user group in a set of user groups corresponding to the model identifier, a centroid for the user group that is determined using a centroid model corresponding to the model identifier is identified. The computing system determines a user group result based at least on the first user profile data, the centroids, and the threshold distance. The user group result is indicative of user group(s) to which to add the user.

Methods and systems for consensus-based online authentication are provided. An encryption device may be authenticated based on an authentication cryptogram generated by the encryption device. The encryption device may transmit a request for security assessment to one or more support devices. The support devices may individually assess the encryption device, other security devices, and contextual information. The support devices may choose to participate in a multi-party computation with the encryption device based on the security assessments. Support devices that choose to participate may transmit one or more secret shares or partial computations to the encryption device. The encryption device may use the secret shares or partial computations to generate an authentication cryptogram. The authentication cryptogram may be transmitted to a decryption device, which may decrypt the authentication cryptogram, evaluate its contents, and authenticate the encryption device based on its contents.

An information processing system capable of processing the encrypted data efficiently is provided. The information processing system of the present invention includes: a key management unit configured to manage a system key; a storage unit configured to store an encryption data encrypted by the system key; and a processing execution unit configured to temporarily construct a virtual execution environment protected from a standard execution environment and decrypt the encryption data in the virtual execution environment based on the system key acquired from the key management unit.

An original certification method according to an embodiment of the present disclosure includes obtaining target data, obtaining a plurality of partial signatures associated with the target data, generating a signature of the target data based on the plurality of partial signatures, and transmitting the target data and the signature to an external device. The plurality of partial signatures are respectively generated based on different private keys among a plurality of private keys, a first private key among the plurality of private keys is stored in a first device, a second private key among the plurality of private keys is stored in a second device, and the first device and the second device are physically separated from each other.

A secure computation device obtains concealed information {M(i.sub.0, . . . , i.sub.S−1)} of a table M(i.sub.0, . . . , i.sub.S−1) having one-variable function values as its members. It is to be noted that M(i.sub.b, 0, . . . , i.sub.b, S−1) generated by substituting counter values i.sub.b, 0, . . . , i.sub.b, S−1 into the table M(i.sub.0, . . . , i.sub.S−1) represents a matrix M.sub.b, γ, μ, which is any one of M.sub.b, 2, 1, . . . , M.sub.b, 3, 2. The secure computation device obtains concealed information {M.sub.b, γ, μ} by secure computation using concealed information {i.sub.b, 0}, . . . , {i.sub.b, S−1} and the concealed information {M(i.sub.0, . . . , i.sub.S−1)}, and obtains concealed information {M.sub.b, Γ, MU} of a matrix M.sub.b, Γ, MU, which is obtained by execution of a remaining process including those processes among a process P.sub.j, 1, a process P.sub.j, 2, a process P.sub.j, 3, and a process P.sub.j, 4, that are performed subsequent to a process P.sub.γ, μ.

Data is efficiently read from a sequence without a read position being revealed. A secure reading apparatus 1 receives a secret text sequence and a secret text of a read position as input, and outputs an element at the read position of the secret text sequence. A vector creating part (12) creates a vector expressing the read position. A compression computing part (13) repeatedly generates a new secret text sequence in which an inner product of a vector based on the secret text sequence and a vector expressing the read position is set as an element. The reading part (14) outputs the new secret text sequence having the number of elements of one as the element at the read position of the secret text sequence.

The techniques described herein may provide an efficient and secure two-party distributed signing protocol for the identity-based signature scheme described in the IEEE P1363 standard. For example, in an embodiment, a method may comprise generating a distributed cryptographic key at a key generation center and a first other device and a second other device and generating a distributed cryptographic signature at the first other device using the second other device.

Methods, systems, and devices for communications are described. A device or a group of devices may generate data. The group of devices may receive a group profile from a node that identifies the devices to be included, and the group profile may include a function to be evaluated at each of the devices. The node may also provision evaluation parameters which may allow the device to provide authenticated aggregate data to a requesting third party, without sharing the data between the devices and without sharing the data with the node, thus concurrently maintaining individual data privacy and data provenance.

A device, system and method for privacy enhanced proximity detection by secure collaboration between a first party without access to user locations and a second party without access to a target user identifier. The second party may receive from the first party a homomorphic encryption public key and homomorphic encrypted target user identifier or masked target location, and may determine an associated homomorphic encrypted target user location. The second party may search a homomorphically encrypt database of user locations and associated user identifiers for homomorphic encrypted proximate user identifiers associated with homomorphic encrypted user locations proximate to the homomorphic encrypted target user location. The second party may send the first user the search result of homomorphic encrypted proximate user identifiers to be decrypted by the first party with a private key to identify proximate user identifiers without knowing their locations.