Nec Corporation

Bar-ilan University

University of Tsukuba

Nec Corporation of America

Hikaru Tsuchida

Toshinori Araki

Kazuma Ohara

Takuma Amada

Toshiyuki Isshiki

Kengo Mori

Jun Furukawa

Isamu Teranishi

Takao Takenouchi

Joseph Keshet

Kazuya Kakizaki

Takashi Nishide

Shir Aviv-Reuven

An individual secure computation server apparatus in a secure computation system computes, by using a cyclic permutation shared by secure computation server apparatuses except one of the secure computation server apparatuses, a value of a cyclic permutation for the one secure computation server apparatus, performs a fraud detection by performing an equality check on values of cyclic permutations computed by the other secure computation server apparatuses, constitutes a random cyclic permutation by synthesizing the cyclic permutations, applies the random cyclic permutation to a share in a sequence, computes a share which indicates an index and to which the random cyclic permutation has been applied by adding a share having a shift amount of the cyclic permutation to the share which indicates the index, reconstructs the share which indicates the index and to which the random cyclic permutation has been applied, and selects the share corresponding to the index.

Secure computation system, secure computation server apparatus, secure computation method, and secure computation program

The invention includes a first learning phase that a machine learning is performed using first dataset to create a learning model f; and a second learning phase that the first or a second dataset is randomly selected; a result by inputting the first and the second datasets to the learning model f is inputted to a discriminator h having a parameter ω; a machine learning is performed using a result and a ground truth data to train the parameter ω, the result being obtained by having the discriminator h discriminate whether the input data belongs to the first or the second dataset; and when the first dataset is selected, a result by inputting the data of the first dataset to the learning model f is inputted to a defender u to train a parameter τ thereof by using an output of the discriminator h through the defender u.

Machine learning method, machine learning system, and program

A secure computation system comprising secure computation server apparatuses, each of which comprises: a discriminant share generation part that computes discriminant shares configured so that an index relating to an input corresponds to a specific value from shares representing the index relating to the input and possible combinations of index shares of an array; a combination configuration part that configures a combination of shares of an element in the array and the discriminant shares for all possible combinations of indices of the array; a shuffle part that shuffles the combinations; a reconstruction part that reconstructs the discriminant shares in the shuffled combinations; and a selection part that selects shares of an element in the array in the combinations where the reconstructed value is the specific value.

An assessment apparatus is able to access a surrogate model generation apparatus that comprises a query generation part that generates a first query causing an assessment target model to make an inference to obtain an inference result; an MIA execution part that executes a membership inference attack using as an input the inference result obtained by sending the first query to the assessment target model and infers virtual training data used to train the assessment target model; and a surrogate model generation part that uses the virtual training data to generate a surrogate model that emulates the behavior of the assessment target model, and the assessment apparatus comprises a security assessment part that transmits a second query to both the surrogate model and the assessment target model to assess the security of the assessment target model using the results therefrom.

Assessment apparatus, surrogate model generation apparatus, assessment method, and program

Among four secure computation nodes holding first to third shares of data in a secret-shared form, first and second secure computation nodes are selected as resharing nodes, and third and fourth secure computation nodes are selected as receiving nodes. The first and second secure computation nodes perform a mini-shuffle for resharing the shares they each hold by using a permutation that third and fourth receiving nodes do not know. Next, the third and fourth secure computation nodes perform a mini-shuffle for resharing the shares they each hold by using a permutation that first and second receiving nodes do not know.

Shuffling shares among nodes to detect incorrectness or frauds

The information processing apparatus comprises a basic operation seed storage part, a reshare value computation part, and a share construction part. The basic operation seed storage part stores a seed for generating a random number used when computation is performed on a share. The reshare value computation part generates a random number using the seed, computes a share reshare value using the generated random number, and transmits data regarding the generated random number to other apparatuses. The share construction part constructs a share for type conversion using the data regarding the generated random number and the share reshare value received from other apparatuses.

Information processing apparatus, secure computation method, and program

Among four secure computation nodes, one secure computation node is selected as a receiving node. Two of three remaining secure computation nodes among the four secure computation nodes are operated as resharing nodes, and a remaining secure computation node is operated as a verifying node. The resharing node(s) performs a mini-shuffle for resharing share(s) held therein by using a permutation that the receiving node does not know and transmits a result(s) of the mini-shuffle to the receiving node. The verifying node computes data to verify the result(s) of the mini-shuffle performed by the resharing node(s) by using a permutation that the receiving node does not know and transmits the data to the receiving node. Shuffling of shares is achieved by repeatedly performing a round as described above so that each of the four secure computation nodes is selected as the receiving node at least once.

Each of a secure computation server apparatuses includes a random number generation part that generates random numbers using a pseudo random number generator shared among the secure computation server apparatuses; a seed storage part that shares and stores a seed(s) used for generating random numbers in the random number generation part; a pre-generated random number storage part that stores random numbers generated by the random number generation part; a share value storage part that stores a share(s) to be a target of processing; a logical operation part that computes a carry to be transmitted and received among the secure computation server apparatuses using the random numbers and the share(s) to be a target of processing; an inner product calculation part that removes a mask from the carry; and an arithmetic operation part that performs a processing of erasing the carry to obtain a processing result.

In a secret computation system, each of the three or more secret computation servers is configured to transmit, to the auxiliary server, carry computation information for computing a carry indicating whether or not digit carry occurs when a share of arithmetic operation is added as a binary number. The auxiliary server is configured to compute the carry based on the carry computation information received and compute an adjustment value used for computing the share of the arithmetic operation from a share of logical operation by using the computed carry. The auxiliary server distributes the computed adjustment value to the three or more secret computation servers. Each of the three or more secret computation servers is configured to convert the share of the logical operation to the share of the arithmetic operation by using a distributed value of the adjustment value.

Secret computation system, secret computation server, auxiliary server, secret computation method, and program

A verification apparatus acquires a source code for multiparty computation, while changing a combination of options settable to a multiparty computation compiler, compiles the source code for each combination of options to generate a plurality of multiparty computation executable codes, selects at least one multiparty computation executable code from the plurality of multiparty computation executable codes as a verification code and provides the at least one verification code to a verification environment of multiparty computation, generates an evaluation index with respect to an execution result of at least one verification code in the verification environment, and selects at least one recommended code from the plurality of multiparty computation executable codes, based on the evaluation index corresponding to at least one verification code and outputs the selected recommended code.

Verification apparatus, multiparty computation verification system, and method and program for verifying multiparty computation executable code

In IaaS (Infrastructure as a Service), when it is desirable to delegate the authority to a user outside a system, a recipient of an access token is designated, thereby preventing illegal distribution of the access token. There is provided an access token system including a generator and a verifier. The generator generates, using secret information of a recipient, a recipient-designated access token for which the recipient is designated, and provides the recipient-designated access token to a user. The verifier verifies that the user who makes access using the recipient-designated access token is the designated recipient.

System, apparatus, method and program for preventing illegal distribution of an access token

When an absolute value of a difference value between a first share and a second share which are secret-shared is less than or equal to a natural number t, the information processing apparatus calculates the difference value between the first share and the second share. Furthermore, the information processing apparatus performs a comparison in magnitude of the first share and the second share using bit-decomposition from a least significant bit to an m-th bit (m being a natural number) of the difference value.

Information processing apparatus, secret calculation method, and program

There is provided a conversion apparatus with which a secure computation execution environment may be easily constructed. The conversion apparatus comprises an input part and a conversion part. The input part inputs a source code. The conversion part converts the input source code so that a secure computation compiler processes it based on setting information relating to secret computation executed by a plurality of secure computation servers.

Conversion apparatus, conversion method and program

There is provided a system for computing a secure statistical classifier, comprising: at least one hardware processor executing a code for: accessing code instructions of an untrained statistical classifier, accessing a training dataset, accessing a plurality of cryptographic keys, creating a plurality of instances of the untrained statistical classifier, creating a plurality of trained sub-classifiers by training each of the plurality of instances of the untrained statistical classifier by iteratively adjusting adjustable classification parameters of the respective instance of the untrained statistical classifier according to a portion of the training data serving as input and a corresponding ground truth label, and at least one unique cryptographic key of the plurality of cryptographic keys, wherein the adjustable classification parameters of each trained sub-classifier have unique values computed according to corresponding at least one unique cryptographic key, and providing the statistical classifier, wherein the statistical classifier includes the plurality of trained sub-classifiers.

Systems and methods for generating and applying a secure statistical classifier

A random number generation server device includes a random number generation unit generating random numbers, a share addition unit generating secret shared data masked using random numbers and the secret shared data of operands in secret equality determination, a secret shared data generation unit generating secret shared data of inputted values, a secret shared data restoration unit obtaining the original values by restoring the secret shared data, and a determination bit-conjunction unit using the secret shared data to perform secret equality determination. A mask value restoration server device includes a secret shared data generation unit, a secret shared data restoration unit, and a determination bit-conjunction unit. A secure computation server device includes a secret shared data generation unit, a secret shared data restoration unit, and a determination bit-conjunction unit.

Growing community of inventors

Tokyo, Japan

Hikaru Tsuchida