A system and method are disclosed for comparing private sets of data. The method includes encoding first elements of a first data set such that each element of the first data set is assigned a respective number in a first table, encoding second elements of a second data set such that each element of the second data set is assigned a respective number in a second table, applying a private compare function to compute an equality of each row of the first table and the second table to yield an analysis and, based on the analysis, generating a unique index of similar elements between the first data set and the second data set.

This disclosure describes techniques for authenticating one or more devices of a user in association with cloud computing services. The techniques include generating credential portions. The credential portions may be used in a signing protocol between one of the user devices and a cloud authenticator. The signing protocol may generate a signature that may be used in authentication with a cloud computing service. In some cases, the credential portions may be shared with other devices of the user. As such, the cloud authenticate may assist multiple user devices to authenticate with the cloud computing service.

This disclosure describes techniques for authenticating one or more devices of a user in association with cloud computing services. The techniques include generating credential portions. The credential portions may be used in a signing protocol between one of the user devices and a cloud authenticator. The signing protocol may generate a signature that may be used in authentication with a cloud computing service. Furthermore, the user may be able to use any one of the user devices to log in to an online service after enrolling only a single user device with the online service. As such, the cloud authenticator may assist multiple user devices to authenticate with the cloud computing service.

Methods and systems for the use of multi-party computation (“MPC”) key systems that involve the use of multiple parties, each of which hold respective private data that may be used to evaluate a computation without ever revealing any of the private data held by each party to perform blockchain operations. Using the MPC key systems, the methods and systems generate secure, encrypted communications across distributed computer networks for authorizing use of cryptography-based digital repositories in order to perform blockchain operations in decentralized applications.

A secret calculation system includes an acquisition unit that acquires each of a plurality of pieces of processing target data indicating data encrypted using a plurality of pieces of different key information generated by a plurality of information processing systems, from a corresponding information terminal of a plurality of information terminals, a secret calculation unit that generates result data indicating a result of calculation based on the plurality of pieces of processing target data in a state where the plurality of pieces of processing target data are encrypted, and a providing unit that provides the result data to the plurality of information terminals.

A multiparty computing system includes at least a first compute node and a second compute node, each of the first compute node and the second compute node each configured to execute a multiparty computation. The first compute node is configured to perform first operations of the multiparty computation over a share of first secret data and a share of second secret data; detect a checkpoint event; and, in response to detection of the checkpoint event, save a state of the multiparty computation on the first compute node to a checkpoint storage. In response to detection of a resume event, the first compute node executes a resume protocol with the second compute node, where the resume protocol includes exchanging messages with the second compute node, and determining, based on the messages, an operation in the multiparty computation to be the starting point to resume the multiparty computation.

A system and method are disclosed for training a recommendation system. The method includes initiating, at a server device, an item-vector matrix V, wherein the item-vector matrix V includes a value m related to a total number of items across one or more client devices and a value d representing a hidden dimension, transmitting the item-vector matrix V to each client device, wherein each client device trains a local matrix factorization model using a respective user vector U and the item-vector matrix V to generate a respective set of gradients on each respective client device, receiving, via a secure multi-party compute protocol, and from each client device, the respective set of gradients, updating the item-vector matrix V using the respective set of gradients from each client device to generate an updated item-vector matrix V and downloading the updated item-vector matrix V to at least one client device.

The embodiments set forth a technique for securely identifying relevant computing devices that are nearby. The technique can be implemented at a first computing device, and include the steps of (1) receiving, from a second computing device, an advertisement packet that includes: (i) a network address that is associated with the second computing device, and (ii) a hash value that is calculated using the network address and an encryption key that is associated with the second computing device, and (2) for each known encryption key in a plurality of known encryption keys that are accessible to the first computing device: (i) calculating a temporary hash value using the network address and the known encryption key, and (ii) in response to identifying that the temporary hash value and the hash value match: carrying out an operation associated with the second computing device.

A system and method for secure searching in a semi-trusted environment by comparing first and second data (query and target data). A first data provider may map first secret data to a first plurality of tokens using a token codebook, concatenate the first plurality of tokens to generate a first token signature, and homomorphically encrypt the first token signature. A second data provider may map second data to a second plurality of tokens using the token codebook, concatenate the second plurality of tokens to generate a second token signature, and compare the homomorphically encrypted first token signature and an unencrypted or homomorphically encrypted second token signature to generate a homomorphically encrypted comparison. A trusted party may decrypt the homomorphically encrypted comparison, using a secret homomorphic decryption key, to determine if the token signatures match or not respectively indicating the search query is found or not in the target data.

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.