A secure sigmoid function calculation system is a system in which map.sub.σ is assumed to be secure batch mapping defined by parameters (a.sub.0, . . . , a.sub.k-1) representing the domain of definition of a sigmoid function σ(x) and parameters (σ(a.sub.0), . . . , σ(a.sub.k-1)) representing the range of the sigmoid function σ(x) (a.sub.0, . . . , a.sub.k-1 are real numbers that satisfy a.sub.0< . . . <a.sub.k-1) and which is configured with three or more secure sigmoid function calculation apparatuses and calculates, from a share [[x.sup..fwdarw.]] of an input vector x.sup..fwdarw., a share [[y.sup..fwdarw.]] of a value y.sup..fwdarw. of a sigmoid function for the input vector x.sup..fwdarw., the system including a secure batch mapping calculating means that calculates the share [[y.sup..fwdarw.]] by [[y.sup..fwdarw.]]=map.sub.σ([[x.sup..fwdarw.]])=([[σ(a.sub.f(0))]], . . . , [[σ(a.sub.f(m-1)]]) (where f(i) (0≤i≤m−1) is j that makes a.sub.j≤x.sub.i<a.sub.j+1 hold).

Systems and methods for generating min-increment counting bloom filters to determine count and frequency of device identifiers and attributes in a networking environment are disclosed. The system can maintain a set of data records including device identifiers and attributes associated with device in a network. The system can generate a vector comprising coordinates corresponding to counter registers. The system can identify hash functions to update a counting bloom filter. The system can hash the data records to extract index values pointing to a set of counter registers. The system can increment the positions in the min-increment counting bloom filter corresponding to the minimum values of the counter registers. The system can obtain an aggregated public key comprising a public key. The system can encrypt the counter registers using the aggregated shared key to generate an encrypted vector. The system can transmit the encrypted vector to a networked worker computing device.

A method for creating a hierarchical threshold signature digital asset wallet using a hierarchical distributed key generator (DKG) and a signature protocol includes steps of generating a public key by users and the digital asset wallet service platform, securing and controlling a portion of shares, sending a transaction signing request, validating the transaction signing request, creating a signature of the signed transaction, and uploading the signed transaction to the corresponding digital asset blockchain network and monitoring the execution of the signed transaction.

Disclosed are systems, methods, and non-transitory computer-readable medium for securely sharing data computations and algorithms. The method may include: receiving, by one or more processors, at least one algorithm function; generating, by the one or more processors, a protection function using the received algorithm function; generating, by the one or more processors, a Boolean circuit function based on the protection function; receiving, by the one or more processors, at least one encrypted data inputs; evaluating, by the one or more processors, the encrypted data inputs using the generated Boolean circuit function to generate evaluated results; and transmitting, by the one or more processors, the evaluated results.

Disclosed herein are systems and methods for synchronizing anonymized linked data across multiple queues for SMPC. The systems and methods guarantee that data is kept private from a plurality of nodes, yet can still be synced within a local queue, across the plurality of local queues. In conventional SMPC frameworks, specialised data known as offline data is required to perform key operations, such as multiplication or comparisons. The generation of this offline data is computationally intensive, and thus adds significant overhead to any secure function. The disclosed system and methods aid in the operation of generating and storing offline data before it is required. Furthermore, the disclosed system and methods can help start functions across multi-parties, preventing concurrency issues, and align secure input data to prevent corruption.

Described herein are a system and techniques for enabling user control over usage of their information by data consumers, even when untrusted parties are involved, while also preventing collusion between the untrusted party and a data consumer. A user's information may be collected by a client device and provided to a host server. An encrypted version of the user'information may be stored at the host server so that it is processed on a private enclave of the host server. When the data is to be provided to multiple data consumers, the data may be encrypted for each of the data consumers and may be released to each of those data consumers simultaneously once confirmation has been received that the data has been made available to each of the data consumers.

An oblivious comparison method takes as input two secret shared numerical values x and y and outputs a secret shared bit that is the result of the comparison of x and y (e.g. 1 if x<y and 0 otherwise). The method uses secure multi-party computation, allowing multiple parties to collaboratively perform the comparison while keeping the inputs private and revealing only the result. The two secret shared values are subtracted to compute a secret shared result, the sign of which indicates the result of the comparison. The method decomposes the secret shared result into a masked Boolean representation and then performs a bit-wise addition of the mask and the masked result. Through the bit-wise addition the method can extract a secret shared representation of the most significant bit, which indicates the sign of the result, without revealing the result itself.

A method, apparatus and computer program product to detect whether specific sensitive data of a client is present in a cloud computing infrastructure is implemented without requiring that data be shared with the cloud provider, or that the cloud provider provide the client access to all data in the cloud. Instead of requiring the client to share its database of sensitive information, preferably the client executes a tool that uses a cryptographic protocol, namely, Private Set Intersection (PSI), to enable the client to detect whether their sensitive information is present on the cloud. Any such information identified by the tool is then used to label a document or utterance, send an alert, and/or redact or tokenize the sensitive data.

A method, and system for a distributed artificial intelligence architecture may be shown and described. An embodiment may present an exemplary distributed explainable neural network (XNN) architecture, whereby multiple XNNs may be processed in parallel in order to increase performance. The distributed architecture may include a parallel execution step which may combine parallel XNNs into an aggregate model by calculating the average (or weighted average) from the parallel models. A distributed hybrid XNN/XAI architecture may include multiple independent models which can work independently without relying on the full distributed architecture. An exemplary architecture may be useful for large datasets where the training data cannot fit in the CPU/GPU memory of a single machine. The component XNNs can be standard plain XNNs or any XNN/XAI variants such as convolutional XNNs (CNN-XNNs), predictive XNNS (PR-XNNs), and the like, together with the white-box portions of grey-box models like INNs.

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.