Abstract: An encryption processing apparatus that processes a ciphertext, the apparatus including a processor that executes a process including: performing a homomorphic operation related to a predetermined operation for three or more of the ciphertexts for which an error range is set to make a range of an error added to a plaintext after the homomorphic operation fall within a predetermined value; and calculating a new ciphertext by applying a predetermined polynomial to a ciphertext that is a result of the homomorphic operation, wherein the calculation includes factorizing each of a plurality of the polynomials into a common polynomial common to the polynomials and an uncommon polynomial not common to the polynomials, and calculating a plurality of the new ciphertexts by using a plurality of ciphertexts calculated by applying the common polynomial to the result of homomorphic operation, and using the uncommon polynomial.

Abstract: A fully homomorphic encryption processing device processing a ciphertext is disclosed, the ciphertext has an integer as a plaintext. The encryption processing device includes a processor that executes a process including: obtaining a ciphertext by using a first polynomial and a second polynomial respectively including a second function and a third function, the second function and the third function being generated by decomposing a first function as a calculation object. The processor also applies the first polynomial to a first ciphertext to obtain a second ciphertext. The processor further applies the second polynomial to a ciphertext obtained by calculation based on applying a predetermined polynomial to the first ciphertext to obtain a third ciphertext. The processor performs a homomorphic operation using the second ciphertext and the third ciphertext to obtain a fourth ciphertext corresponding to a result of calculation of the first function which uses the first ciphertext as input.

Abstract: To achieve efficient reading of data from a memory including a plurality of banks by specifying different banks and accessing the memory from a plurality of hash computation circuits simultaneously, an information processing device includes a memory 1 including a plurality of banks, a plurality of hash computation circuits 8, and an interconnect 2 respectively connecting the banks in the memory 1 and the hash computation circuits 8 to each other, wherein the hash computation circuits 8 execute control in such a manner that read requests for reading data from the memory 1 respectively include bank numbers for specifying different banks in the same cycle.

Abstract: An encryption processing device processes a ciphertext that has two values as a plaintext and is a ciphertext of fully homomorphic encryption that allows a logical operation without decryption. The encryption processing device executes a predetermined operation including calculating a plurality of new ciphertexts based on the ciphertexts input thereto. The encryption processing device comprising a processor which executes a process including performing a first homomorphic operation for the input ciphertext, calculating a first ciphertext having a polynomial by using a predetermined polynomial for a result of the first homomorphic operation to extract a second ciphertext having a coefficient of a plaintext polynomial of the first ciphertext, extracting a third ciphertext having another coefficient of a plaintext polynomial of the first ciphertext, and performing a homomorphic operation using the second ciphertext and the third ciphertext to calculate a fourth ciphertext.

Abstract: An encryption processing device processing a ciphertext is disclosed. The ciphertext is a fully homomorphic ciphertext that has a value obtained by adding an error with a predetermined variance to a predetermined value, as a plaintext associated with an integer and that enables a predetermined operation between integers without decryption, and the predetermined operation is division of a first ciphertext as a dividend and a second ciphertext as a divisor. The encryption processing device includes a processor which executes a process including calculating a third ciphertext corresponding to a reciprocal of the divisor by using a first polynomial for the second ciphertext, and performing multiplication based on the first ciphertext and the third ciphertext to calculate a fourth ciphertext corresponding to a result of division of the first ciphertext and the second ciphertext as a result of multiplication.

Abstract: An encryption processing device that processes a ciphertext, the ciphertext being a fully homomorphic ciphertext that has a value obtained by adding an error with a predetermined variance to a predetermined value, as a plaintext associated with an integer and that enables a predetermined operation between integers to be performed without decryption, the device including a processor which executes a process including generating, based on a first ciphertext as a multiplier, a third ciphertext having a first stepped polynomial as a plaintext and performing an operation based on the third ciphertext and a second ciphertext as a multiplicand to calculate a ciphertext corresponding to a result of multiplication between plaintexts of the first ciphertext and the second ciphertext as a fourth ciphertext of an operation result.

Abstract: To speed up an operation by a full adder that achieves full homomorphic encryption, a ciphertext processed by an encryption processing device has two values as a plaintext and is a ciphertext of fully homomorphic encryption that allows various operations to be performed by performing a logical operation without performing decryption. The encryption processing device includes an operation unit that performs a logical operation for a ciphertext received by a receiving unit and an output unit that outputs a result of the operation by the operation unit. The operation unit reduces the number of logical operations (homomorphic operations) by temporarily using a specific ciphertext that can have multiple values as a plaintext.

Abstract: An operation by a full adder that achieves fully homomorphic encryption is speeded up. Ciphertexts are ciphertexts of fully homomorphic encryption that allow a logical operation to be performed without decryption. A sorting process is performed which rearranges the ciphertexts arranged in a predetermined order in accordance with a predetermined method. In the sorting process, a homomorphic operation based on a first ciphertext and a second ciphertext is performed. From a result of this homomorphic operation and a ciphertext obtained based on a predetermined polynomial, new first and second ciphertexts are calculated. When the first and second ciphertexts and the new first and second ciphertexts satisfy a predetermined condition, the first ciphertext and the second ciphertext are swapped.

Abstract: An information processing apparatus that processes a priority granting process, the information processing apparatus includes a memory unit that memorizes a verification key for verifying an electronic signature for priority approval generated by using a private key of a priority seller that grants a prioritized approval authority to a transaction. The information processing apparatus further includes a processor which executes a process including verifying using the verification key, an electronic signature given to an unapproved transaction publicized on a distributed ledger, generating when an electronic signature given to the unapproved transaction is verified by the verifying as the electronic signature for priority approval, a block that preferentially includes the unapproved transaction over unapproved transactions to which the electronic signature for priority approval is not given, and publicizing the block on the distributed ledger.

Abstract: A learning method, a learning model, a classifier, a generator, and a processing system are provided, which consider human vision in learning using a machine learning model for an image. The learning method learns a machine learning model that inputs or outputs image data with data for learning that includes both or either one of image data in which a component that is difficult to judge visually is left out and image data in which a noise component that is difficult to judge visually is added at a predetermined ratio.

Abstract: An information processing system includes a first apparatus and a second apparatus. The first apparatus includes a decision unit and a first publicizing unit. The decision unit decides secret information. The first publicizing unit publicizes a first transaction including limitation information enabling retrieving of crypto-assets by using the secret information on a distributed ledger. The second apparatus includes a receiving unit, a second publicizing unit, and an executing unit. The receiving unit receives the secret information. The second publicizing unit receives crypto-assets when the secret information is received by the receiving unit after the first transaction is publicized on a distributed ledger and by publicizing a second transaction including the secret information received by the receiving unit on a distributed ledger. The executing unit performs a predetermined operation in response to reception of the secret information by the receiving unit.

Abstract: To provide a learning apparatus and an inferring apparatus that can prevent problems such that a computation amount increases, efficiency of a learning process decreases, and an inferring result does not have high accuracy. A learning apparatus and an inferring apparatus include a predictable area determining unit that determines whether target data has a predictable area in which an inferring result of an inferring process can be easily predicted. In the learning apparatus and the inferring apparatus, predetermined data processing is performed on a data area that is determined to be a predictable area, data required to infer an area that is not a predictable area is output to a machine learning model, and data processing is performed in an average time shorter than that of the inferring process.

Abstract: A trading system includes a first trading apparatus including a first processor, and a second trading apparatus including a second processor. The first processor encrypts data using secret information. The first processor publishes second trading information on a distributed ledger when first trading information enabling reception of crypto assets on a condition of publication of argument information that enables derivation of the secret information is published on a distributed ledger. The second trading information is information for receiving the crypto assets and includes the argument information. The second processor publishes the first trading information on a distributed ledger. The second processor acquires the argument information when the second trading information is published on a distributed ledger. The second processor decrypts encrypted data using the argument information.

Abstract: In a learning that uses a machine learning model for an image, a learning method, a learning model, a classifier, and a generator in which human vision is taken into consideration are provided. The learning method learns a machine learning model that inputs or outputs image data with data for learning that includes training data subjected to a process of leaving out a component that is difficult to visually judge to reduce an information amount or generated data at a predetermined ratio.

Abstract: A learning apparatus that can realize efficient machine learning is provided. A learning apparatus that learns a set value in a machine learning model based on predetermined image data for learning includes an inverting unit that inverts data of at least a part of respective channels in the image data for learning, an input unit that inputs the inverted data to the machine learning model, an output unit that can compare data obtained by inverting data output from the machine learning model with training data, and/or data output from the machine learning model with data obtained by inverting training data, and a learning process executing unit that learns the set value according to a result of the comparison.

Abstract: An arithmetic apparatus includes an interface and a circuity. The interface is connected to an information processing apparatus that is connected to a client apparatus and that processes data in an encrypted state. The circuitry acquires, from the information processing apparatus, encryption input data or encryption target data encrypted with a first encryption key. The circuitry decrypts the acquired, encryption input data or encryption target data with a first decryption key. Then, the circuitry executes a predetermined arithmetic operation on the decrypted arithmetic operation target data, encrypts data of an arithmetic operation result obtained by the predetermined arithmetic operation with the first encryption to key, and outputs the encrypted data of the arithmetic operation result to the information processing apparatus.

Abstract: To provide a display processing device that displays the structure of a displayed item configured by stacking a plurality of displayed items in an understandable manner and enables easy confirmation of each of the layers being stacked and displayed. The display processing device includes a graduation-image display unit (a graduation-display processing unit) 24 causing a display device 15 to display a graduation image having values the number of which corresponds to the number of a plurality of image layers, a specification unit (a pointer-display processing unit) 25 causing a value on the graduation image to be specified, and a displayed-number determination unit (a number-of-displayed-layers determination processing unit) 26 determining the number of image layers that are to be stacked and displayed by the display device 15, among the plurality of image layers in accordance with the value on the graduation image specified by the specification unit 25.

Abstract: A technique capable of providing a new function usable as an activation function is provided. An inference apparatus includes a receiving unit that receives input of target data; and an inference unit that executes a predetermined inference process with respect to the target data using a neural network model. The neural network model includes a plurality of processing layers, and, as the processing layers, one or more activation function layers that convert an input value by a predetermined activation function. The activation function of at least one of the activation function layers is configured as a function of a waveform the output value of which changes, in a first range, to approach a maximum value as an input value increases and, in a second range, away from a minimum value as the input value increases, such that the output values in the first and second ranges are not the same.

Abstract: A processing apparatus includes at least one processor configured to function as: an input unit that receives encrypted data based on homomorphic encryption; and a process execution unit that executes a predetermined process by using the encrypted data while maintaining a secret state and includes one or more processing units. At least one of the processing units is a multiplication corresponding processing unit for executing a calculation corresponding to a processing of multiplying plaintext data by a predetermined multiplier.

Abstract: An inference apparatus that executes a predetermined inference process using a neural network model including: an input unit configured to receive encryption data; and a processor configured to execute a process including: executing predetermined process with respect to the encryption data using the neural network model, wherein the neural network model has a processing layer that executes a process of generating second encryption data from a plurality of pieces of first encryption data input from a previous stage and outputting the second encryption data, the second encryption data providing, by being decrypted, a processing result obtained by performing operations with modulo operation with respect to plurality of pieces of plain text data corresponding to the plurality of pieces of first encryption data, the neural network model being configured such that a nonlinear function is not applied to the second encryption data in a posterior stage of the processing layer.