Nippon Telegraph and Telephone Corporation

Ihi Corporation

Ntt Corporation

Tohoku University

Taiheiyo Cement Corporation

Tocalo Co., Ltd.

Koki Hamada

Dai Ikarashi

Ryo Kikuchi

Koji Chida

Atsunori Ichikawa

Naoto Kiribuchi

Hiroyuki Kamata

Ibuki Mishina

Takuya Yoshinoya

Satoshi Hasegawa

Gen Takahashi

Hiroaki Ohara

Masao Nagasaki

Kazuharu Misawa

Naoki Sato

Tomohiro Kaneko

Shigeki Sakakura

Hitoshi Fuji

Yusuke Takeuchi

Akihisa Yano

Nobuyuki Honma

Katsumi Takahashi

Tatsuo Suidzu

Yoshiaki Matsuzawa

Tatsuya Oka

Nobuhiko Kubota

Gembu Morohashi

Junichi Tomida

Kunitaka Masaki

Hiroki Sudo

Satoshi Tanaka

Asami Miyajima

Rie Harada

Kensuke Kitazawa

Masanobu Kii

Takashi Nishii

Kazuhiro Takeuchi

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

Secret sigmoid function calculation system, secret logistic regression calculation system, secret sigmoid function calculation apparatus, secret logistic regression calculation apparatus, secret sigmoid function calculation method, secret logistic regression calculation method and program

A secure maximum value computation apparatus, assuming that a set X={[[x]], [[x]], . . . , [[x]]}, includes an output unitthat outputs [[x]] and [[1]] as a maximum secret value [[y]] and a flag [[z(x)]], respectively, when n=1 holds, a comparison unitthat computes a comparison result of which is larger with respect to a predetermined order for each pair {[[x]], [[x]]}⊂X of elements of the X, a flag computation unitthat computes whether all comparison results related to each of the [[x]]s are 'large' for each of the [[x]]s to set a computed value as a flag [[z(x)]], and a maximum value computation unitthat uses the [[z(x)]] to computes a maximum value [[y]].

Secret maximum value calculation apparatus, method and program

A secure collation system performs secure-data-collation between first and second information processing apparatuses and includes the first and second information processing apparatuses. The second information-processing-apparatus creates, when receiving a first vector having a hash value of a key value of the first information-processing-apparatus as an element, a second vector by adding a dummy hash value to the first vector and rearranging the first vector by random permutation; creates a third vector having, as elements, a hash value of a key value of the second information-processing-apparatus and a hash value of a dummy key value; and transmits the second and third vectors to the first information-processing-apparatus. The first information-processing-apparatus calculates a hash value of an element of the third vector and creates a fourth vector having the hash value as an element; and collates matched values between each element of the third vector and each element of the fourth vector.

Secure consolidation system, information processing apparatus, secure consolidation method, and program

A calculation of a gradient descent method in secure computing is performed at high speed while maintaining accuracy. A secure gradient descent computation method calculates a gradient descent method while keeping a gradient and a parameter concealed. An initialization unit initializes concealed values [M], [V] of matrices M, V (S). A gradient calculation unit determines concealed value [G] of a matrix G of a gradient g (S). A parameter update unit calculates [M] β1 [M]+(1−β1) [G] (S-), calculates [V]←β2 [V]+(1−β2) [G]◯[G] (S-), calculates [M{circumflex over ( )}]←β{circumflex over ( )}1, t [M] (S-), calculates [V{circumflex over ( )}]←β{circumflex over ( )}2, t [V] (S-), calculates [G{circumflex over ( )}]←Adam ([V{circumflex over ( )}]) (S-), calculates [G{circumflex over ( )}]←[G{circumflex over ( )}]◯[M{circumflex over ( )}] (S-), and calculates [W]←[W]−[G{circumflex over ( )}] (S-).

Machine learning using secure gradient descent computation

A secure computation system includes first, second, and third secure-computation-apparatuses each having a tripartite-share of a concealed input vector. The first secure-computation-apparatus converts its tripartite-share into a bipartite-share with the third secure-computation-apparatus; calculates a third vector obtained by subtracting a second permutation of a first vector and a second vector from its permutation of its bipartite-share; and transmits the third vector and the second permutation to the second secure-computation-apparatus. The third secure-computation-apparatus converts its tripartite-share into a bipartite-share with the first secure-computation-apparatus; calculates a fourth vector obtained by adding the first vector to a first permutation of its bipartite-share; transmits the fourth vector to the third secure-computation-apparatus: set the second vector as a bipartite-share with the second secure-computation-apparatus. The second secure-computation-apparatus sets a vector obtained by adding the second permutation of the fourth vector to the third vector, as a bipartite-share with the third secure-computation-apparatus.

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

Calculation time is reduced without degrading approximation accuracy in calculation of a complicated function through secure computation. A secret batch approximation system calculates a concealed text [z] of an approximate value z for a function value y satisfying yj=f(xj) by using a concealed text [x] of a value x as input. g is defined as a polynomial for approximating each section of m sections into which the function f is divided. A parameter acquisition unit acquires a concealed text [a] of a parameter a corresponding to the value x for each integer j that is not less than 1 and not more than n, where aj is defined as a parameter pi corresponding to a section Ri including a value xj. A polynomial calculation unit calculates a polynomial g([x], [a]) by using the concealed text [x] of the value x as input based on the concealed text [a].

Secret batch approximation system, secure computation device, secret batch approximation method, and program

A server determines an array [[addr]] indicating a storage destination of each piece of data, generates an array of concealed values, and connects the generated array to the array [[addr]] to determine an array [[addr']]. The server generates a sort permutation [[σ]] for the array, applies the sort permutation [[σ]] to the array [[addr′]], and converts the array [[addr′]] into an array with a sequence composed of first Z elements set to [[i]] followed by αelements set to [[B]]. The server generates a sort permutation [[σ]] for the converted array [[addr′]], generates dummy data, imparts the generated dummy data to the concealed data sequence, applies the sort permutations [[σ]] and [[σ]] to the data array imparted with the dummy data, and generates, as a secret hash table, a data sequence obtained by deleting the last N pieces of data from the sorted data array.

Secret hash table construction system, reference system, methods for the same

A secret grouping apparatus which classifies a plurality of elements into one or more groups includes a processor configured to receive a target vector in which the plurality of elements are disposed so that elements belonging to a same group are continuous, a group information vector representing a last element in the group, and a classification destination vector representing a classification destination of each of the elements in the group; calculate a detection vector representing a last element of elements classified into a same classification destination in the group by using the target vector, the group information vector, and the classification destination vector; and stably sort the target vector and the detection vector with respect to the classification destination vector to create a target vector after classifying each of the elements into the classification destination and a group information vector representing a last element in a group after the classification.

Secret grouping apparatus, secret grouping system, secret grouping method, and program

A combustion gas bleeding probe includes a gas pipe for bleeding a part of a combustion gas from a kiln, and a plurality of discharge ports each of which is provided penetrating through the gas pipe and each of which discharges a low-temperature gas in a direction that is perpendicular to a direction of flow of a bleed gas bled by the gas pipe and that is directed toward a center of the flow of the bleed gas. The discharge ports discharge the low temperature gas such that a ratio of a momentum of the low-temperature gas per discharge port to a momentum of the bleed gas satisfies 1.2 to 4.0, and a value (m-1) obtained by dividing a ratio of a wind speed of the low-temperature gas to a wind speed of the bleed gas by an inner diameter of the gas pipe satisfies 1.5 to 3.5.

Combustion gas bleeding probe and method for operating same

A search key is generated (S). A key relationship array is transmitted (S). If an element matching the key relationship array is present, the found search key is held (S). A key relationship index is transmitted (S). A record read out using the key relationship index is transmitted (S). If the record matches the search key, the found search key is held (S). The found search key is set for an empty element of the key relationship array and is transmitted (S). A data array is transmitted (S). If an element matching the data array is present, the found data is held (S). A data index is transmitted (S). A record read out using the data index is transmitted (S). If the record matches the search key, the found data is held (S). Desired data is set for an empty element of the data array and is transmitted (S).

Data access method, data storage system, server apparatus, client apparatus, and program

The encrypted data analysis device includes a sorting unit that sorts by [Time Information] and then sorts by [User ID] an encrypted data set group including a plurality of encrypted data sets, each of the plurality of encrypted data sets including a [Location ID], the [User ID], and the [Time Information], an encoding unit that generates a [Flow], and encoding the [Location ID] extracted, and an equal sign determination unit that determines whether a [User ID] and another [User ID] adjacent to each other are equal, and when not equal, replaces a corresponding [Flow] with a [predetermined value that represents invalid].

Encrypted data analysis device, encrypted data analysis method, and program

Access to an array is efficiently performed without reveling an accessed position. A storagestores an array of concealed values [x'] of an array x′and an array of addresses a′corresponding to respective elements of the array of concealed values [x′]. A refresh unitdetermines a concealed value [F] of a random parameter F, an array of concealed values [x] of an array xgenerated by permutating the array x′with random permutation ρ, and an array of public tags bcalculated from respective elements of the array of addresses awith the function Tag. An access unitperforms a desired access to an element of the array of concealed values [x] corresponding to a tag that is calculated from a concealed value [j] of an access position j with the function Tag and the concealed value [F] of the parameter.

Secure array access apparatus, secure array access method, and program

An analysis query response system comprises a user terminalthat generates and transmits an analysis query, and a database apparatusincluding an analysis query verification apparatusthat includes a verification execution partthat performs a first verification of whether the analysis query satisfies a predetermined privacy preservation indicator, and an analysis query execution apparatusthat includes a personal data storage partthat stores personal data and an analysis query execution and preservation partthat, in a case where the first verification is successful, performs an analysis corresponding to the analysis query on the personal data read from the personal data storage part to acquire an analysis result, and applies a predetermined privacy-preserving mechanism to the acquired analysis result.

Analysis query response system, analysis query execution apparatus, analysis query verification apparatus, analysis query response method, and program

The present invention provides techniques to calculate the number of surviving and the number of deaths while still concealing survival time data. The present invention includes: a group data position calculation means configured to calculate a share [[g]] of a sequence gand a share [[g]] of a sequence grepresented by predetermined equations from a share [[g]] of a sequence g of values of group of survival time data included in a survival time data set D; a group data number calculation means configured to calculate a share [[s]] and a share [[s]] from a share [[t]] of a sequence t of values of time of survival time data included in the survival time data set D, the share [[g]], and the share [[g]], by [[s]]=GroupSum ([[g]], [[t]]), [[s]]=GroupSum ([[g]], [[t]]); and a survival number calculation means.

Secret survival data processing system, secret survival data processing apparatus, secret survival data processing method, and program

A technique for performing secure computation of a sigmoid function with high speed and precision is provided. A secret sigmoid function calculation system is a system, in which g(x) is assumed to be a secure computable function, which calculates, from a share [[x]] of an input value x, a share [[σ'(x)]] of a value of a sigmoid function for the input value x, and includes: a first comparing means that generates a first comparison result [[c]]=less_than([[x]], t); a second comparing means that generates a second comparison result [[d]]=greater_than([[x]], t); a first logical computation means that generates a first logical computation result [[e]]=not([[c]]); a second logical computation means that generates a second logical computation result [[k]]=and([[c]], [[d]]) or [[k]]=mul([[c]], [[d]]); and a function value calculating means that calculates the share [[σ′(x)]]=mul([[k]], [[g(x)]])+[[e]].

Secret sigmoid function calculation system, secret logistic regression calculation system, secret sigmoid function calculation apparatus, secret logistic regression calculation apparatus, secret sigmoid function calculation method, secret logistic regression calculation method, and program

Growing community of inventors

Musashino, Japan

Koki Hamada