Abstract: A multi-party privacy computing method and device based on semi-trusted hardware, wherein the method applied to semi-trusted hardware comprises the following steps: acquiring random number masks and random seeds of all user terminals; generating a garbled circuit seed according to the random seeds; generating a garbled circuit according to a predetermined circuit description and the garbled circuit seed, wherein the garbled circuit comprises garbled tables, wire labels and decoding information; sending the wire labels corresponding to the inputs of all user terminals to a user terminal corresponding to the semi-trusted hardware by using an oblivious transfer protocol; and sending the garbled table and the decoding information to the user terminal corresponding to the semi-trusted hardware, so that the user terminal can compute an output value according to the garbled tables, the decoding information and the wire label corresponding to the inputs of all user terminals.

Abstract: A multi-party privacy computing method and device based on semi-trusted hardware, wherein the method applied to semi-trusted hardware comprises the following steps: acquiring random number masks and random seeds of all user terminals; generating a garbled circuit seed according to the random seeds; generating a garbled circuit according to a predetermined circuit description and the garbled circuit seed, wherein the garbled circuit comprises garbled tables, wire labels and decoding information; sending the wire labels corresponding to the inputs of all user terminals to a user terminal corresponding to the semi-trusted hardware by using an oblivious transfer protocol; and sending the garbled table and the decoding information to the user terminal corresponding to the semi-trusted hardware, so that the user terminal can compute an output value according to the garbled tables, the decoding information and the wire label corresponding to the inputs of all user terminals.

Abstract: A method and a device for remote acquisition of correlated pseudo-random numbers based on semi-trusted hardware. When applied to semi-trusted hardware, the method comprises: acquiring a random seed of a sender and a selected number of a receiver; generating a plurality of first correlated pseudo-random numbers and first commitment seeds according to the random seed and a predetermined category of the correlated pseudo-random number; generating a first commitment value and a first open value by a commitment mechanism according to the first correlated pseudo-random numbers and the first commitment seeds; generating a Merkle proof according to the first commitment value and the selected number; sending the first correlated pseudo-random numbers, the first commitment value, the first open value and the Merkle proof corresponding to the selected number to the receiver.

Abstract: A method and a device for remote acquisition of correlated pseudo-random numbers based on semi-trusted hardware. When applied to semi-trusted hardware, the method comprises: acquiring a random seed of a sender and a selected number of a receiver; generating a plurality of first correlated pseudo-random numbers and first commitment seeds according to the random seed and a predetermined category of the correlated pseudo-random number; generating a first commitment value and a first open value by a commitment mechanism according to the first correlated pseudo-random numbers and the first commitment seeds; generating a Merkle proof according to the first commitment value and the selected number; sending the first correlated pseudo-random numbers, the first commitment value, the first open value and the Merkle proof corresponding to the selected number to the receiver.

Abstract: A computer-implemented method for processing input data in a mapreduce framework includes: receiving, in the mapreduce framework, a data processing request for input data; initiating, based on the data processing request, a map operation on the input data by multiple mappers in the mapreduce framework, each of the mappers using an aggregator to partially aggregate the input data into one or more intermediate key/value pairs; initiating a reduce operation on the intermediate key/value pairs by multiple reducers in the mapreduce framework, wherein, without sorting the intermediate key/value pairs, those of the intermediate key/value pairs with a common key are handled by a same one of the reducers, each of the reducers using the aggregator to aggregate the intermediate key/value pairs into one or more output values; and providing the output values in response to the data processing request.

Abstract: A computer-implemented method for processing input data in a mapreduce framework includes: receiving, in the mapreduce framework, a data processing request for input data; initiating, based on the data processing request, a map operation on the input data by multiple mappers in the mapreduce framework, each of the mappers using an aggregator to partially aggregate the input data into one or more intermediate key/value pairs; initiating a reduce operation on the intermediate key/value pairs by multiple reducers in the mapreduce framework, wherein, without sorting the intermediate key/value pairs, those of the intermediate key/value pairs with a common key are handled by a same one of the reducers, each of the reducers using the aggregator to aggregate the intermediate key/value pairs into one or more output values; and providing the output values in response to the data processing request.