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.

A method of multi-party computation, for processing and secure handing of a plurality of data associated with one or more users, comprising the steps of: providing a predetermined multi-party computation algorithm; each user being able to send a first dataset to a data processing unit via a respective second data processing unit distinct from the first data processing unit and in signal communication with the first data processing unit; each first dataset being associated with the user and comprising one or more encrypted numerical values; processing each first dataset that has been sent using at least one reference function residing in the first data processing unit to generate a respective encrypted result for each reference function; requesting the first data processing unit to send the result using a predetermined function shared by the users and a respective second data processing unit sending the result to the second requesting data processing unit; wherein the step of sending the first dataset comprises the substeps of detecting the presence of decimal numerical values and integer numerical values among the numerical values of the first dataset; associating an integer mantissa and an exponent of a floating-point representation with each decimal numerical value that has been detected; encrypting each integer numerical value and each mantissa using the predetermined multi-party computation algorithm.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for allowing suitable digital components to be automatically selected and provided to a client device. Methods can include generating a universal identifier for a digital component that is presented in the application. The application updates a set of universal identifiers that has been created for digital components presented by the application over a specified time period. The application identifies digital components and the corresponding universal identifiers that are blocked and generates a probabilistic data structure representing the set of blocked universal identifiers. The application creates multiple shares of the probabilistic data structure and transmits different shares to different servers. The application receives a separate response generated by each of the different servers based on the multiple shares and identifies a digital component to present in the application based on a combination of the separate responses.

Disclosed are techniques for determining data relationships between privacy-restricted datapoints, sourced over a computer network, which require data privacy measures concealing at least some datapoints from other clients in the network that the datapoint respectively do not originate from. A first client encrypts a first datapoint with a public key of a public/private encryption scheme and communicates it to the second client along with the public key. The second client encrypts a corresponding second datapoint with the public key, then determines a relationship between the two encrypted datapoints, and communicates the determined relationship to a central client along with the public key. Random noise is encrypted by the central client and added to the determined relationship, then sent together to the first client, followed by decryption by the first client using the private key. The central client extracts the random noise after receiving the decrypted determined relationship.

A method for performing privacy-preserving or secure multi-party computations enables multiple parties to collaborate to produce a shared result while preserving the privacy of input data contributed by individual parties. The method can produce a result with a specified high degree of precision or accuracy in relation to an exactly accurate plaintext (non-privacy-preserving) computation of the result, without unduly burdensome amounts of inter-party communication. The multi-party computations can include a Fourier series approximation of a continuous function or an approximation of a continuous function using trigonometric polynomials, for example, in training a machine learning classifier using secret shared input data. The multi-party computations can include a secret share reduction that transforms an instance of computed secret shared data stored in floating-point representation into an equivalent, equivalently precise, and equivalently secure instance of computed secret shared data having a reduced memory storage requirement.

A secret computation system is a secret computation system for performing computation while keeping data concealed, and comprises a cyphertext generation device that generates cyphertext by encrypting the data, a secret computation device that generates encrypted basic statistics by performing secret computation of predetermined basic statistics using the cyphertext while keeping the cyphertext concealed, and a computation device that generates decrypted basic statistics by decrypting the encrypted basic statistics and performs predetermined computation using the decrypted basic statistics.

This document describes a secure machine learning platform. In some aspects, a method includes transmitting by the application to the machine learning platform, a set of data including a user profile, one or more characteristics of a digital component, contextual signals, model identifier, and data indicating a type of event. The application receives a request generated based on the computer-readable instructions to upload a user profile of a user of the client device to a machine learning platform. The computer-readable instructions initiate the request in response to detecting an occurrence of the event with the digital component. In response to the request, the application can obtain the user profile request data element that includes a model identifier for a machine learning model and one or more characteristics of at least one of the digital component or the first content page.

A method comprises receiving an ad event data including data about a plurality of ad events, and including a user ID and an ad ID for each ad event in the ad event data set, where the ad event data set has been anonymized applying a one-way encryption key for each user ID in the ad event data set, and a two-way encryption key for the ad ID in the ad event data set. The attribution processor receives a customer data set including data about a plurality of customers, including a user ID and a customer value for each customer, where the customer data set has been anonymized using the one-way encryption key for each user ID in the data, and a private encryption key for the customer value. Without decrypting the received ad event data set and the received customer data set, the processor then matches ad events for each conversion by comparing the user IDs in the encrypted ad event data set to the user IDs in the encrypted customer data set to create a set of contributing ad events, assigns a share of the customer value to each relevant ad event, sums homomorphically the encrypted customer values for contributing events, and determines a recommendation for serving advertisements.

Disclosed is a system (100, 200) for authenticating a digital transaction by implementing a multi-party computation protocol. The system comprises a first set of first nodes (102) configured to generate a first data (104), wherein each of the first nodes, from the first set of first nodes, in a second set of first nodes (102A) is an independent party and a server arrangement (106) communicably coupled with the first set of first nodes. The server arrangement is configured to receive the first data from the first set of first nodes, generate a second data (108), verify whether a number of other of the first nodes (102B) not in the second set of first nodes is equal to or at most only one greater than a number of first nodes in the second set of first nodes and authenticate the digital transaction based on the verification, using the secret shares in the first data and the second data.

A system and method are disclosed for each party of a group of m parties to be able to learn an Nth smallest value in a combined list of the values in which each party has separate lists of values. A method includes creating, by each party of a group of m parties, m lists of additive shares associated with each party's respective list of data, distributing, from each party to each other party in the group of m parties, m−1 of the lists of additive shares to yield a respective combined list of additive shares W.sub.i obtained by each party of the m parties, receiving from a trusted party a list of additive shares V.sub.i associated with a hot-code vector V, computing, in a shared space by each party, a respective R.sub.i value using a secure multiplication protocol and comparing, in the shared space, by each party and using secure multi-party comparison protocol, the respective R.sub.i to all elements in the respective combined list of additive shares W.sub.i to yield a total number P.sub.i of values in W.sub.i that are smaller than R.sub.i. The value P.sub.i is used to either end the method or loop back for further processing with new values of W.sub.i and in some cases a new value of N.