Abstract: A cold iron source melting ratio estimation device that estimates a melting ratio of a cold iron source charged into a converter-type refining furnace during refining of molten iron in the converter-type refining furnace. The device includes: an input section to which measured values of in-furnace information or estimated values of the in-furnace information is input, the in-furnace information including a molten iron temperature and a carbon concentration in the molten iron during refining; a database section that stores a model equation and parameters related to a refining reaction of the molten iron in the converter-type refining furnace; a computation section that computes the melting ratio of the cold iron source using the measured values or the estimated values input to the input section; and an output section that displays the melting ratio of the cold iron source computed by the computation section.

Abstract: Provided is a technology for performing secure computation of a random number generation method using weighted probability distribution with high accuracy while keeping data secure. The technology includes: first vector computation means that computes a share ([[p?1]], . . . , [[p?L]]) from a share ([[p1]], . . . , [[pL]]) by using prefix sum; uniform random number generation means that generates a share ([[q1]], . . . , [[qS]]) of a vector (q1, . . . , qS) (where qi (i=1, . . . , S) is a uniform random number, and satisfies 0?qi?1) having a uniform random number as an element; and random number computation means that computes a share ([[r1]], . . . , [[rS]]) of a vector (r1, . . . , rS) having an output value as an element from the share ([[p?1]], . . . , [[p?L]]), a share ([[x1]], . . . , [[xL]]), and the share ([[q1]], . . . , [[qS]]) by using secure collective mapping.

Abstract: Provided is a technology for performing secure computation of a k-means method with high accuracy while keeping data secure. The technology includes: centroid table initialization means that sets a table including a set of a share of a cluster ID j and a share of a centroid of a cluster ID j as a j-th record as an initial value of a centroid table; distance table computation means that computes a distance table including a set of a share of a data ID i, the share of the cluster ID j, a share of a distance dij, between data of the data ID i and the centroid of the cluster ID j as an M(j?1)+i-th record; cluster ID table computation means that computes a cluster ID table including a set of the share of the data ID i and a share of a cluster ID k(i) of the cluster to which the data of the data ID i belongs as an i-th record; and centroid table computation means that computes the centroid table.

Abstract: A vehicle behavior control method is suitable for a vehicle behavior control device. The vehicle behavior control device includes: a lateral acceleration sensor, detecting lateral acceleration occurring in a vehicle body; a wheel speed sensor, detecting a wheel speed of a wheel; a steering angle sensor, detecting a steering angle of the wheel; a steering angle lateral acceleration calculation unit, calculating steering angle lateral acceleration from the wheel speed and the steering angle; and a yaw moment control unit, applying yaw moment to the vehicle body. In the vehicle behavior control method, when the lateral acceleration and the steering angle lateral acceleration meet a predetermined condition, a yaw moment directed inward in a turning direction of the vehicle body is applied by the yaw moment control unit.

Abstract: Provided is a clustering apparatus capable of securely performing hierarchical clustering while concealing all of a calculation process and values in the middle.

Abstract: 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].

Abstract: 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 [[gA]] of a sequence gA and a share [[gB]] of a sequence gB represented 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 [[sA]] and a share [[sB]] 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 [[gA]], and the share [[gB]], by [[sA]]=GroupSum ([[gA]], [[t]]), [[sB]]=GroupSum ([[gB]], [[t]]); and a survival number calculation means.

Abstract: In a temperature control apparatus for controlling the temperature of a load by supplying high-temperature circulating liquid to the load, a device in which a helical channel portion of a first heat exchange channel through which circulating liquid flows is housed in a second heat exchange channel formed of a channel space in a hollow shell through which coolant flows is used as a heat exchanger for cooling the circulating liquid, cylindrical members are individually fitted on an inflow channel portion and an outflow channel portion connected to opposite ends of the helical channel portion of the first heat exchange channel, and the cylindrical members are each fixed to the shell of the heat exchanger with a weld.

Abstract: 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 [[??(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]], t1); a second comparing means that generates a second comparison result [[d]]=greater_than([[x]], t0); 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 [[??(x)]]=mul([[k]], [[g(x)]])+[[e]].

Abstract: A secure computation device 1n of the secure computation system includes a first calculation unit 11n, a second calculation unit 12n, a third calculation unit 13n, a fourth calculation unit 14n, and an output unit 15n. By calculation being performed in cooperation of these, a group by max operation or a group by min operation can be performed on a table to which a flag is added.

Abstract: In a voting system including a vote broadcasting server and a plurality of client terminals, the vote broadcasting server generates a plurality of pieces of vote information that is data in which secret information is shared and transmits the vote information to each client terminal, each client terminal generates voting information by encrypting vote information such that the vote information can be decrypted when predetermined conditions are satisfied and transmits the voting information to the vote broadcasting server, the vote broadcasting server transmits a voting information group composed of voting information received from respective client terminals to the respective client terminals, and each client terminal ascertains a vote result according to the number of pieces of vote information decrypted from the voting information group and executes processing based on the vote result.

Abstract: A secure joining system is a secure joining system including a plurality of secure computing apparatuses. The plurality of secure computing apparatuses include a first vector joining unit, a first permutation calculation unit, a first vector generation unit, a second vector joining unit, a first permutation application unit, a second vector generation unit, a first inverse permutation application unit, a first vector extraction unit, a second permutation application unit, a third vector generation unit, a second inverse permutation application unit, a second vector extraction unit, a modified second table generation unit, a third permutation application unit, a fourth vector generation unit, a shifting unit, a third inverse permutation application unit, a bit inversion unit, a third vector extraction unit, a modified first table generation unit, a first table joining unit, and a first table formatting unit.

Abstract: A share generating device obtains N seeds s0, . . . , sN?1, obtains a function value y=g(x, e)?Fm of plaintext x?Fm and a function value e, and obtains information containing a member yi and N?1 seeds sd, where d?{0, . . . , N?1} and d?i, as a share SSi of the plaintext x in secret sharing and outputs the share SSi. It is to be noted that the function value y is expressed by members y0?Fm(0), . . . , yN?1?Fm(N?1) which satisfy m=m(0)+ . . . +m(N?1).

Abstract: Provided is a technique for performing statistical processing such as processing for obtaining parameters of logistic regression analysis faster than before. A secure statistical processing system includes a cross tabulation table computing device 2 that performs secure computation on a cross tabulation table in which frequencies are in plain texts while keeping each record concealed; and a statistical processing device 3 that performs predetermined statistical processing using the cross tabulation table in which frequencies are in plain texts. The cross tabulation table computing device 2 may include a plurality of secure computation devices 221, . . . , 22N that perform secure computation on a cross tabulation table in which frequencies are fragments subjected to secret sharing while keeping each record concealed, and a management device 21 that restores the fragments to compute the cross tabulation table in which frequencies are in plain texts.

Abstract: A parameter estimation device for executing a parameter estimation of a cox proportional hazard model by secure computation comprises: a data storage unit that stores a database having, with respect to each object to be observed, a record including a point of time at which an event was observed, a feature amount of an object to be observed at the point of time, and a state of the object to be observed at the point of time; a calculation unit that, by reading a vector comprising points of time from the database, and sorting the vector, generates a replacement table and a flag indicating a boundary between the points of time, by using the replacement table and the flag, totalizer the feature amounts at the respective points of time while concealing values at the points of time, and performs the parameter estimation on the basis of a result of the totalization; and an output unit that outputs a parameter estimated by the calculation unit.

Abstract: A share [x]i of plaintext x in accordance with Shamir's secret sharing scheme is expressed by N shares [x0]i, . . . , [xN?1]i, and each share generating device Ai obtains a function value ri=Pm(i(?))(si) of a seed si, obtains a first calculated value ?i=?(i, i(?))[xi(?)]i+ri using a Lagrange coefficient ?(i, i(?)), a share [xi(?)]i, and the function value ri, and outputs the first calculated value ?i to a share generating device Ai(?). Each share generating device Ai accepts a second calculated value ?i(+), obtains a third calculated value zi=?(i, i(+))[xi]i+?i(+) using a Lagrange coefficient ?(i, i(+)), a share [xi]i, and the second calculated value ?i(+), and obtains information containing the seed si and the third calculated value zi as a share SSi of the plaintext x in secret sharing and outputs the share SSi.

Abstract: There are provided a prediction apparatus, a prediction system, and a prediction method, which predict, by an easy method, whether there is a possibility of an animal contracting a disease in a near future.

Abstract: A power is computed at high speed with a small number of communication rounds. A secret computation system that includes three or more secret computation apparatuses computes a share [a?] of the ?-th power of data “a” from a share [a] of data “a” while data “a” is concealed. The share [a] of data “a” and an exponent ? are input to an input unit (step S11). A local operation unit computes the pu-th power of a share [at] of the t-th power of data “a” without communication with the other secret computation apparatuses (step S12). A secret computation unit uses secret computation that requires communication with the other secret computation apparatuses to compute a multiplication in which at least one of the multiplicands is [ a ( t * p ^ u ) ] , the computation result of the local operation unit, to obtain the share [a?] (step S13). An output unit outputs the share [a?] (step S14).

Abstract: 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].

Abstract: 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 [[?1]] for the array, applies the sort permutation [[?1]] 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 ?i elements set to [[B]]. The server generates a sort permutation [[?2]] for the converted array [[addr?]], generates dummy data, imparts the generated dummy data to the concealed data sequence, applies the sort permutations [[?1]] and [[?2]] 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.