A method includes obtaining, from a server, a filter including a set of encrypted identifiers each encrypted with a server key controlled by the server. The method includes obtaining a request that requests determination of whether a query identifier is a member of a set of identifiers corresponding to the set of encrypted identifiers. The method also includes transmitting an encryption request to the server that requests the server to encrypt the query identifier. The method includes receiving, from the server, an encrypted query identifier including the query identifier encrypted by the server key and determining, using the filter, whether the encrypted query identifier is not a member of the set of encrypted identifiers. When the encrypted query identifier is not a member of the set of encrypted identifiers, the method includes reporting that the query identifier is not a member of the set of identifiers.

Described embodiments provide systems and methods for validating a request to perform an action to access at least one file. A computing device can receive a request from the client, the request being to perform an action to access at least one file and including a first computed value indicative of one or more previous actions on files. The computing device may compare the first computed value to a second computed value maintained by the computing device independently from the first computed value. The second computed value may be indicative of the one or more previous actions on the files. The computing device may perform secondary authentication in addition to primary authentication for the client, responsive to an indication of trustworthiness of the client or the file according to the comparison of the first computed value to the second computed value.

Systems, devices, software, and methods of the present invention provide for homomorphically encrypted (HE) and other data represented as polynomials of degree K−1 to be transformed in O(K*log(K)) time into ‘unique-spiral’ representations in which both linear-time (O(K)) addition and linear-time multiplication are supported without requiring an intervening transformation. This capability has never previously been available and enables very significant efficiency improvements, i.e., reduced runtimes, for applications such as Fully Homomorphic Encryption (FHE), Post-Quantum Cryptography (PQC) and Artificial Intelligence (AI). Other efficient operations, such as polynomial division, raising to a power, integration, differentiation and parameter-shifting are also possible using the unique-spiral representations. New methods are introduced based on the unique-spiral representation that have applications to efficient polynomial composition, inversion, and other important topics.

It is provided a federated learning system for aggregating gradient information representing a result of training an AI model in an edge device, the federated learning system comprising the edge device and a server apparatus, the training module in the edge device being configured to generate an edge switch share in which the encrypted aggregated gradient is encrypted, and to transmit the generated edge switch share to the server apparatus, the encryption/decryption module in the server apparatus being configured to generate an encrypted aggregated gradient for decryption by adding edge switch shares received from the plurality of the edge device, generate an aggregated gradient by decrypting the generated encrypted aggregated gradient for decryption, and to transmit the generated aggregated gradient to the edge device, the training module in the edge device being configured to train the AI model by using the aggregated gradient received from the server apparatus.

A public-key scheme of Homomorphic Encryption (HE) in the framework Quotient Algebra Partition (QAP) comprises: encryption, computation and decryption. With the data receiver choosing a partition or a QAP, [n, k, C], a public key Key.sub.pub=(VQ.sub.en, Gen.sub.ε) and a private key Key.sub.priv=.sup.†P.sup.† are produced, where VQ.sub.en is the product of an n-qubit permutation V and an n-qubit encoding operator Q.sub.en, Gen.sub.ε an error generator randomly provides a dressed operator Ē=V.sup.†EV spinor error E of [n, k, C]. Then, by Key.sub.pub, the sender can encode his k-qubit plaintext Ix) into an n-qubit ciphertext |ψ.sub.en, which is transmitted to the cloud. The receiver prepares the instruction of encoded computation U.sub.en=PV.sup.†Q.sub.en.sup.† for a given k-qubit action M and sends to cloud, where is the error-correction operator of [n, k, C], =I.sub.2.sub.n−k.Math.M the tensor product of the (n−k)-qubit identity I.sub.2.sub.n−k and M , and V.sup.†Q.sup.†.sub.en and P the complex-transposes of VQ.sub.en and

A system and method for homomorphic encryption in a healthcare network environment is provided and includes receiving digital data over the healthcare network at a data custodian server in a plurality of formats from various data sources, encrypting the data according to a homomorphic encryption scheme, receiving a query at the data custodian server from a data consumer device concerning a portion of the encrypted data, initiating a secure homomorphic work session between the data custodian server and the data consumer device, generating a homomorphic work space associated with the homomorphic work session, compiling, by the data custodian server, a results set satisfying the query, loading the results set into the homomorphic work space, and building an application programming interface (API) compatible with the results set, the API facilitating encrypted analysis on the results set in the homomorphic work space.

Systems and methods are shown for providing private local sponsored content selection and improving intelligence models through distribution among mobile devices. This allows greater data gathering capabilities through the use of the sensors of the mobile devices as well as data stored on data storage components of the mobile devices to create predicted models while offering better opportunities to preserve privacy. Locally stored profiles comprising machine intelligence models may also be used to determine the relevance of the data gathered and in improving an aggregated model for identifying the relevance of data and the selection of sponsored content items. Distributed optimization is used in conjunction with privacy techniques to create the improved machine intelligence models. Publishers may also benefit from the improved privacy by protecting the statistics of type or volume of sponsored content items shown with publisher content.

A method for providing ciphertext data by a first computing device having memory includes obtaining, from the memory, plaintext data having a structure; providing the plaintext data to a structure preserving encryption network (SPEN) to generate the ciphertext data, where the structure of the plaintext data corresponds to a structure of the ciphertext data; and communicating, from the first computing device to a second computing device, the ciphertext data to permit analysis on the ciphertext data.

A control method executed by a computer, including: when accepting execution instructions for a first task and a second task, adding an operation code that causes execution of an operation, to a processing program that corresponds to the first task, and generating a first program that includes the processing program and the operation code; encrypting a second program that corresponds to the second task to generate encrypted data, by using an operation result obtained based on the execution of the operation; and transmitting the first program and the encrypted data to a device that corresponds to the first task.

Privately determining whether a password satisfies a constraint without having to divulge the password itself to a third party that evaluates the constraint, and without the third party even being aware of the result of the evaluation. After the user selects a password, private communication (e.g., private information retrieval) is used to determine whether the selected password satisfies password constraints. For instance, the password might be encrypted (e.g., homomorphically), and then the encrypted password and a function definition (e.g., a homomorphic function definition) is then provided to the third party. The third party then performs the function and returns an already encrypted result. The third party generated the encrypted result directly, without having access to the result in the clear. Upon receiving the encrypted result, the user's computing system may then decrypt the result, to find out whether the password satisfies the constraints, and thus is sufficiently safe.