A system allows the identification and protection of sensitive data in a multiple ways, which can be combined for different workflows, data situations or use cases. The system scans datasets to identify sensitive data or identifying datasets, and to enable the anonymisation of sensitive or identifying datasets by processing that data to produce a safe copy. Furthermore, the system prevents access to a raw dataset. The system enables privacy preserving aggregate queries and computations. The system uses differentially private algorithms to reduce or prevent the risk of identification or disclosure of sensitive information. The system scales to big data and is implemented in a way that supports parallel execution on a distributed compute cluster.

A method for comparing a first and a second databases to determine whether an individual is represented by both an element of the first database and an element of the second database, wherein said elements are biometric data, including the implementation of the steps applying a classification model for each element of each database so as to construct a set of first and second bins of the respective first and second databases, each bin bringing together similar elements, each first bin being associated with a second bin; comparing the elements from the first database for at least one pair of an associated first bin and second bin belonging to said first bin with the elements from the second database belonging to said second bin, at least one of the first and the second databases then being encrypted homomorphically.

An electronic device is disclosed. The electronic device comprises: a memory for storing at least one instruction; and a processor for executing at least one instruction, wherein the processor executes the at least one instruction so as to, when an operation command for a homomorphic ciphertext is input, obtain an operation result by using a plaintext operation corresponding to the operation command and a plaintext corresponding to the homomorphic ciphertext, and output the obtained operation result in a manner corresponding to the operation command.

Systems and methods for privacy-preserving inventory matching are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for inventory matching may include: (1) receiving, from each of a plurality of clients, a masked submission comprising an identification of at least one security to buy or sell and a desired quantity to buy or sell; (2) aggregating the masked submissions resulting in a sum of the desired quantities to buy or sell; (3) matching at least two of the clients to conduct a transaction based on aggregation and their respective masked submissions; and (4) conducing the transaction between the matched clients.

Disclosed herein are system, method, and computer program product embodiments for performing a set of operations on one or more encrypted numbers to be an approximation of performing an algebraic operation on the one or more encrypted number. A server can receive from a client, a public key of a fully homomorphic encryption scheme and one or more encrypted numbers, and perform a set of operations comprising a square root function, a rectified linear activation function (ReLU), or a multiplicative inverse function on the one or more encrypted numbers to generate an encrypted operational result. The encrypted operational result generated by the set of operations can be an approximation of performing an algebraic operation on the one or more encrypted number. The server can further transmit to the client the encrypted operational result.

Methods and systems for reducing noise in homomorphic multiplication include: receiving a plurality of ciphertexts, each having a corresponding level; receiving data specifying a homomorphic multiplication on two ciphertexts; for two ciphertexts having different levels: adjusting a scaling factor of a first ciphertext so that the respective scaling factors of the two ciphertexts are the same; performing the homomorphic multiplication; and rescaling a result of the homomorphic multiplication; for two ciphertexts having the same level: performing the homomorphic multiplication; rescaling a result of the homomorphic multiplication; and using the scaling factors of the two ciphertexts during a decryption process.

In an embodiment, a method comprises intercepting, from a first computer, a first set of instructions that define one or more original operations, which are configured to cause one or more requests to be sent if executed by a client computer; modifying the first set of instructions to produce a modified set of instructions, which are configured to cause a credential to be included in the one or more requests sent if executed by the client computer; rendering a second set of instructions comprising the modified set of instructions and one or more credential-morphing-instructions, wherein the one or more credential-morphing-instructions define one or more credential-morphing operations, which are configured to cause the client computer to update the credential over time if executed; sending the second set of instructions to a second computer.

A method for evaluating data is based on a computational model, the computational model comprising model data, a training function and a prediction function. The method includes training the computational model by: receiving training data and training result data for training the computational model, and computing the model data from the training data and the training result data with the training function. The method includes predicting result data by: receiving field data for predicting result data; and computing the result data from the field data and the model data with the prediction function. The training data may be plaintext and the training result data may be encrypted with a homomorphic encryption algorithm, wherein the model data may be computed in encrypted form from the training data and the encrypted training result data with the training function. The field data may be plaintext, wherein the result data may be computed in encrypted form from the field data and the encrypted model data with the prediction function.

Disclosed is an operation device. The operation device includes a memory storing at least one instruction; and a processor configured to execute the at least one instruction, and the processor, by executing the at least one instruction, may perform encoding or decoding for an approximate homomorphic ciphertext using a predetermined matrix having only a half of an element of a matrix corresponding to a canonical embedding function.

Disclosed is a multi-source encrypted image retrieval method based on federated learning and secret sharing, including the following steps: S1. performing model training on a convolutional neural network of double cloud platforms based on federated learning, with an image owner joining the double cloud platforms as a coalition member; and S2. completing, by an authorized user, encrypted image retrieval based on additive secret sharing with the assistance of the double cloud platforms. The present disclosure provides a multi-source encrypted retrieval scheme based on federated learning and secret sharing, which simplifies the neural network model structure for retrieval by using federated learning, to obtain better network parameters. Better neural network parameters and a more simplified network model structure are achieved by compromising overheads on the image owner side, such that a better convolutional neural network can be used in encrypted image retrieval.