A range determination unit (412) takes as input an input ciphertext C.sub.i resulting from encrypting input data, determines whether a value obtained from the input data is within a reference range, and generates a range ciphertext a.sub.j depending on a determined result. A result generation unit (413) performs a homomorphic operation on the range ciphertext a.sub.j generated by the range determination unit (412), so as to generate a result ciphertext D of a result of performing inference including a non-polynomial operation on the input data. An output unit (414) outputs the result ciphertext D.

A method of securely accessing a database with sensitive data, such as the clinical information of patients, by a client in a privacy-preserving manner, including: communicating with the server to obtain tags for specific attribute-value pairs when the client is authorized to make a query; imposing a tag quota per client and restricting tag generation to authorized query terms with valid digital signatures from a third-party authority; storing the tags and their associated query terms in confidence for future queries; sending a combination of tags that define the terms of a conjunctive query over a secure channel to a proxy; receiving from the proxy encrypted coefficients of a polynomial whose roots are indices to the query results; decrypting the encrypted coefficients in a first protocol with the server; calculating the roots of the polynomial based upon the decrypted coefficients and discarding any superfluous roots; obtaining the encrypted records associated with the calculated roots from the proxy; and decrypting the encrypted records in a second protocol with the server.

A PQ signature scheme MMSAT that is capable of aggregating and compressing unrelated messages signed individually by different parties. The scheme extends the notion of multi-signatures, which are signatures that support aggregation of signatures on a single message signed by multiple parties.

A method for secure validation of machine learning models and parallel validation data using homomorphic encryption can include providing, by a provider, a machine learning model and providing, by a user, validation data; encrypting, by the provider, the machine learning model; sending, by the provider, a public encryption parameter to the user; selecting, by the user and provider, a unifying encoding method; encrypting, by the user, the validation data; sending, by the user, the encrypted validation data; processing, the encrypted validation data with the encrypted machine learning model; providing encrypted results of said processing to the provider and the user; and decrypting the results and evaluating whether the performance of the machine learning model is satisfactory with the given valuation data of the user.

A first system receives an encrypted data vector representing a text search query from a second system and second encrypted data from a third system that may include a first vector and a second vector representing text of an electronic document. The first system may multiply the vectors by a random vector. The first system may determine a first difference between the encrypted data vector and the first vector, and a second difference between the encrypted data vector and the second vector. The first system may determine a product of the first and second difference. The first system may send the product to the third system and then receive a value representing the decrypted difference. The first system may determine if the value satisfies a condition and send the result of the determination to the second system.

Disclosed are methods and systems to provide public and private-key leveled Fully Homomorphic Encryption (FHE) systems using Hensel Codes and p-adic and g-adic properties for encryption and decryption that also provide for homomorphic arithmetic operations on encrypted ciphertexts. A source device may encrypt the ciphertext of a message using Hensel Codes, then deliver the ciphertext to either a destination device or an intermediary device. When the intermediary device receives the ciphertext from the source device, the intermediary device may homomorphically perform Hensel Code arithmetic computations with the ciphertext and at least one additional ciphertext and send the result ciphertext to the destination device. The destination device decrypts the ciphertext, giving the original message when no computations have been performed by the intermediary device, or the unencrypted result equivalent to the unencrypted computations performed on the ciphertexts by the intermediary device.

Systems and memory devices are disclosed for fully homomorphic encryption (FHE). The system may include a processing unit including: a data memory for storing coefficients for a polynomial; a twiddle factor (TF) memory for storing TF values associated with the polynomial; a TF register connected to the TF memory; a plurality of first registers connected to the data memory; a plurality of first MUXs connected to the first registers; a plurality of second registers connected to the plurality of first MUXs; a plurality of Butterfly (BF) cores connected to the plurality of the second registers and the TF register; wherein each of the plurality of BF cores is configured to, responsive to a control signal, perform a Butterfly Transform (BFT) operation based on two coefficients from the data memory and a TF value from the TF memory.

A security platform architecture is described herein. A user identity platform architecture which uses a multitude of biometric analytics to create an identity token unique to an individual human. This token is derived on biometric factors like human behaviors, motion analytics, human physical characteristics like facial patterns, voice recognition prints, usage of device patterns, user location actions and other human behaviors which can derive a token or be used as a dynamic password identifying the unique individual with high calculated confidence. Because of the dynamic nature and the many different factors, this method is extremely difficult to spoof or hack by malicious actors or malware software.

An encryption key generating method and apparatus based on homomorphic encryption, and a ciphertext operation method and apparatus using the generated encrypt key are disclosed. The method of generating an encryption key for performing encryption based on homomorphic encryption includes receiving data, generating a first encryption key and a second encryption key used for encrypting the data based on a secret key, and transmitting the first and second encryption keys.

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.