Information processing method and information processing system

Abstract

The present technology relates to an information processing method and an information processing system that can obtain data with higher reliability. This information processing method includes a step for causing the information processing system to store first data and second data, which is associated with the first data and is more difficult to alter than the first data, in a P2P database in association with each other. The present technology can be applied to information processing systems.

Description

Information processing method and information processing system

[0001] The present technology relates to an information processing method and an information processing system, and more particularly to an information processing method and an information processing system that enable more reliable data to be obtained.

[0002] Traditionally, user-related data, such as user behavior history data and image data, has been used for various purposes, including various services and marketing. When using such data, it is important to ensure the authenticity (authenticity) of the data.

[0003] For example, as a technology for storing a user's behavioral history, a technology has been proposed in which data blocks containing movement history information representing the user's movement history are linked and stored as a hash chain in chronological order for each area from which the user moved and for each area to which the user moved (see, for example, Patent Document 1).

[0004] Japanese Patent Application Laid-Open No. 2021-22278

[0005] Incidentally, data relating to a user, such as behavior history data obtained from a global positioning system (GPS), which can be generally obtained, can often be easily tampered with or deleted on the user's terminal or the like.

[0006] For example, as in Patent Document 1, even if the behavioral history is stored as a hash chain after being uploaded, if the behavioral history can be tampered with before being uploaded, the credibility of the behavioral history cannot necessarily be said to be high.

[0007] The present technology has been developed in light of such circumstances, and makes it possible to obtain more reliable data.

[0008] An information processing method according to a first aspect of the present technology includes a step in which an information processing system associates second data, which is related to first data and is more difficult to tamper with than the first data, with the first data and stores the second data in a P2P database.

[0009] In a first aspect of the present technology, second data that is related to first data and is more difficult to tamper with than the first data is associated with the first data and stored in a P2P database.

[0010] An information processing method according to a second aspect of the present technology includes a step in which an information processing system acquires storage data including second data that is related to first data and is more difficult to tamper with than the first data, and the first data, and compares the acquired storage data with data corresponding to the storage data stored in a P2P database.

[0011] In a second aspect of the present technology, storage data including second data related to first data and more difficult to tamper with than the first data, and the first data, is acquired, and the acquired storage data is compared with data corresponding to the storage data stored in a P2P database.

[0012] An information processing system according to a third aspect of the present technology is an information processing system including an information processing device corresponding to the information processing method according to the first aspect of the present technology and an information processing device corresponding to the information processing method according to the second aspect of the present technology.

[0013] FIG. 1 is a diagram illustrating an example of the configuration of an information processing system. FIG. 2 is a diagram illustrating the storage of NFTs in a blockchain. FIG. 3 is a flowchart illustrating data storage processing. FIG. 4 is a flowchart illustrating data provision processing and data matching processing. FIG. 5 is a flowchart illustrating data storage processing. FIG. 6 is a diagram illustrating an example of the configuration of a computer.

[0014] Hereinafter, embodiments to which the present technology is applied will be described with reference to the drawings.

[0015] First Embodiment About the Present Technology Data relating to a user, such as user behavior history data and image data captured by a user, can be used for various services, marketing, and the like.

[0016] However, if the reliability (authenticity) of data related to a user is low, it becomes difficult to utilize such data for marketing, etc. Also, for example, it is conceivable that a user may use behavior history data as evidence of their own behavior history for some reason, but if the reliability of the behavior history data cannot be guaranteed, it will be difficult to use it as evidence.

[0017] Therefore, in this technology, in addition to first data whose authenticity (genuineness) is to be proven, storage data is generated that includes second data that was acquired simultaneously or approximately simultaneously with the first data and is more difficult to tamper with than the first data, and an NFT (Non-Fungible Token) that includes the storage data is stored in the blockchain. In this way, more reliable storage data can be obtained as data related to the user. In other words, the authenticity (genuineness) of the first data can be guaranteed.

[0018] Here, the first data to be certified for authenticity (hereinafter also referred to as the data to be certified) may be, for example, the user's location information, user information indicating the name of the device's user (user name), or image data of an image (still image or moving image) taken by the user.

[0019] The second data is data related to the first data (certification target data) and serves to guarantee (certify) the authenticity of the first data. Hereinafter, the second data will also be referred to as certification data as appropriate. Specifically, the certification data may be, for example, an RF fingerprint or biometric information.

[0020] An RF fingerprint is RF fingerprint information that represents the characteristics of the radio signal transmitted when a device such as a mobile terminal performs RF (Radio Frequency) communication, i.e., wireless communication using RF.

[0021] The RF fingerprint varies depending on the frequency (channel) and signal strength of the communication. In other words, the RF fingerprint varies depending on the location and time of the wireless communication device. Therefore, it is difficult to tamper with such an RF fingerprint.

[0022] For example, suppose that a device that acquires or generates the data to be certified acquires an RF fingerprint at or near the same time as the acquisition or generation of the data to be certified. In this case, the RF fingerprint becomes data that is associated with the data to be certified, with respect to the timing of acquisition and the device from which it was acquired.

[0023] Therefore, if such an RF fingerprint is used as certification data, certification data related to the certification target data can be obtained, i.e., certification data that can only be obtained at the time the certification target data is acquired or generated and on the device that acquired or generated the certification target data can be obtained.

[0024] Biometric information is information specific to a user that is obtained from the user (living body). For example, possible biometric information includes facial features extracted from a face image of the user, fingerprint features extracted from a fingerprint image of the user, iris features extracted from an iris image of the user, a vein shape pattern extracted from an image of the user's hand, a pulse rate, a voiceprint, etc.

[0025] Biometric information is information unique to a user that cannot be impersonated, that is, biometric information is information (data) that is difficult to tamper with.

[0026] For example, suppose that a device that acquires or generates certification target data acquires biometric information of a user operating (holding) the device at the same time or approximately at the same time as the acquisition or generation of the certification target data. In this case, the biometric information becomes data that is associated with the certification target data in terms of the acquisition time and the user.

[0027] Therefore, by using such biometric information as certification data, it is possible to certify (guarantee) which user the certification target data (first data) relates to.

[0028] In other words, if biometric information is used as certification data, the certification data can be obtained only from the user who was involved in the acquisition or generation of the certification target data. Furthermore, depending on the type of biometric information, such as pulse rate, the biometric information obtained can only be obtained at the time the certification target data was acquired or generated.

[0029] The certification data (second data) is not limited to an RF fingerprint or biometric information, but may also be environmental data obtainable from the surrounding environment at the time of acquisition or generation of the certification target data. The environmental data here refers to, for example, images and sounds, temperature, humidity, the presence or concentration of chemical substances such as gases, etc., around the device that acquired or generated the certification target data.

[0030] In this technology, proof data (second data) is acquired simultaneously (or approximately simultaneously) with the acquisition or generation of proof target data (first data). Furthermore, the proof target data and the proof data are immediately linked after acquisition, and storage data including the proof target data and the proof data is generated.

[0031] By generating the storage data as described above, it is possible to prevent tampering with the storage data (certification target data) even before uploading the storage data.

[0032] Furthermore, a predetermined conversion process is performed on the storage data, and an NFT is generated that includes the data obtained by the conversion process (hereinafter also referred to as converted data).

[0033] The NFT, which is non-fungible data (information) generated in this way, is then stored on the blockchain. This makes it possible to prevent tampering with the NFT, i.e., the storage data, after uploading. Storing such an NFT on the blockchain can also be said to associate the data to be certified with the certification data and store them on the blockchain.

[0034] The conversion process may be any process, such as lossless conversion or lossy conversion. Specifically, the conversion process may be a hashing process using a hash function, an encryption process, a compression encoding process, etc. A password or the like may be set for the conversion process.

[0035] In addition, while the following describes an example of storing an NFT in a blockchain, the storage destination of an NFT is not limited to a blockchain and can be anything. For example, a blockchain is an example of a P2P database, which is a distributed file system that uses a P2P (Peer to Peer) network, but an NFT can also be stored in a P2P database other than a blockchain.

[0036] By storing an NFT on the blockchain, the authenticity (genuineness) of the storage data, i.e., the data to be certified, can be verified using the NFT. In other words, the NFT (converted data), which is the data corresponding to the storage data, can be verified against the storage data.

[0037] For example, when a hashing process is performed as the above-mentioned conversion process, a verification terminal that verifies (confirms) the authenticity acquires storage data from a user's device, etc., performs a hashing process on the acquired storage data, and generates converted data (hash value).

[0038] The verification terminal then compares the converted data it generated with the converted data contained in the NFT read from the blockchain, and confirms whether the converted data match.

[0039] Furthermore, for example, if encryption processing is used as the above-mentioned conversion processing, the verification terminal performs processing related to the encryption processing based on the NFT read from the blockchain to verify the authenticity (authenticity) of the data to be certified.

[0040] According to the present technology, data to be stored is generated by linking data to be certified, which may be easily tampered with, with data for proof, which is more difficult to tamper with than the data to be certified. Furthermore, an NFT is generated that includes the data to be stored, more specifically, the converted data obtained from the data to be stored, and is stored in the blockchain.

[0041] By doing so, it becomes difficult to tamper with the storage data both before and after uploading, and therefore it is possible to obtain storage data (certification target data) with higher reliability.

[0042] As a result, more reliable, i.e., higher quality, attestation data can be used in, for example, marketing.

[0043] In this case, for example, a company that uses the data to be certified can easily take measures such as offering some kind of return in exchange for the data to be certified provided by the user. For example, since it is difficult to tamper with the data to be certified, the company can increase the return rate when giving the return to the user.

[0044] Users can also use the data to be proven as evidence. For example, if users encounter some kind of trouble between themselves, they can use location information, image data, and other data to be proven as evidence. In addition, the data to be proven can also be used as evidence in police investigations or court cases, helping to protect users from false accusations.

[0045] An example of a use case of this technology will be described.

[0046] Use case 1: Proof of behavioral history Data to be proven (first data): location information, user information Data for proof (second data): RF fingerprint, biometric information

[0047] The first use case is to prove the authenticity (credibility) of a user's behavior history, which consists of location information and user information.

[0048] In this example, for example, location information or device user information to be certified is linked to RF fingerprint or biometric information, which are difficult to tamper with, and the stored data is then used to calculate a hash value for that data. An NFT containing the resulting hash value is then generated and managed on the blockchain.

[0049] This makes it difficult to tamper with the NFT, i.e., the behavioral history, before and after uploading. To prove the authenticity of the behavioral history, the user provides the storage data stored on the user's device to a verification terminal, which then hashes the provided storage data and compares the resulting hash value with the hash value included in the NFT on the blockchain.

[0050] Use case 2: Proving the authenticity of image data Data to be certified (first data): image data, user information, location information Data for certification (second data): RF fingerprint, biometric information

[0051] The second use case is to prove the authenticity (credibility) of an image taken by a user.

[0052] In this example, image data of an image taken by a user is linked to user information (photographer information), location information, and RF fingerprint and biometric information as verification data to create storage data. Furthermore, an NFT containing the storage data, more specifically an NFT containing a hash value of the storage data, is generated and uploaded to the blockchain, and the authenticity of the image data (image) is verified using the NFT in the same manner as in Use Case 1.

[0053] This makes it possible to certify that an image based on image data as certification target data was taken by the user at that location.

[0054] In use case 2, for example, in news reports, it is possible to determine whether images that have been made public or images provided by others and are being made public are actually images taken by a specific user in a specific location, and whether they are fake videos or materials obtained from the Internet, etc.

[0055] <Configuration of Information Processing System> A more specific embodiment to which the present technology is applied will be described below. In particular, the following will be described as an example in which user behavior history data is generated as storage data and hashing processing is performed as conversion processing.

[0056] FIG. 1 is a diagram showing an example of the configuration of an embodiment of an information processing system to which the present technology is applied.

[0057] The information processing system 11 shown in FIG. 1 includes a mobile terminal 21, a confirmation terminal 22, and a blockchain 23.

[0058] In the information processing system 11, a mobile terminal 21, a confirmation terminal 22, and a blockchain 23 are interconnected via a network.

[0059] The mobile terminal 21 is an information processing device that acquires or generates certification target data and generates storage data based on the certification target data. For example, the mobile terminal 21 is a smartphone, a tablet terminal, a personal computer, a smart watch, a game console, etc.

[0060] The mobile terminal 21 includes a communication unit 31, a location information acquisition unit 32, an RF fingerprint acquisition unit 33, a biometric information acquisition unit 34, a storage unit 35, and a data processing unit 36.

[0061] The communication unit 31 communicates with external devices such as the confirmation terminal 22 and the blockchain 23 via a network. Note that the communication unit 31 of the mobile terminal 21 may communicate directly with the confirmation terminal 22 without going through a network.

[0062] The location information acquisition unit 32 is formed of, for example, a GPS sensor, and acquires location information indicating the location of the mobile terminal 21 .

[0063] The RF fingerprint acquisition unit 33 acquires an RF fingerprint indicating characteristics of a wireless signal transmitted from the communication unit 31 during wireless communication from the communication unit 31. The biometric information acquisition unit 34 is composed of sensors such as a camera (image sensor), an optical pulse sensor, and a sound collection sensor, and acquires biometric information from the user operating the mobile terminal 21.

[0064] The storage unit 35 is made up of a non-volatile memory or the like, and stores various data such as storage data generated by the mobile terminal 21 .

[0065] The data processing unit 36 ​​is composed of, for example, a processor, and performs various types of data processing. For example, the data processing unit 36 ​​generates behavior history data as storage data from the acquired location information, etc., and performs conversion processing on the behavior history data to generate an NFT.

[0066] The confirmation terminal 22 is an information processing device that verifies the authenticity (genuineness) of the storage data generated by the mobile terminal 21, particularly the certification target data included in the storage data. For example, the confirmation terminal 22 is a smartphone, a tablet terminal, a personal computer, a smart watch, a game console, or the like.

[0067] The confirmation terminal 22 has a communication unit 51 , a behavior history data acquisition unit 52 , a data processing unit 53 , and a data collating unit 54 .

[0068] The communication unit 51 communicates with external devices such as the mobile terminal 21 and the blockchain 23 via a network.

[0069] The behavior history data acquisition unit 52 to the data matching unit 54 are realized by, for example, a processor.

[0070] The behavior history data acquisition unit 52 acquires behavior history data (storage data) from the mobile terminal 21 through communication with the mobile terminal 21 via the communication unit 51 .

[0071] The data processing unit 53 performs the same conversion process as in the data processing unit 36 ​​on the behavior history data acquired from the mobile terminal 21 by the behavior history data acquisition unit 52 .

[0072] The data matching unit 54 verifies the authenticity (genuineness) of the behavioral history data, i.e., the data to be certified, by matching the converted data contained in the NFT read from the blockchain 23 with the converted data obtained by the conversion process by the data processing unit 53.

[0073] In other words, the data matching unit 54 matches the behavior history data provided by the mobile terminal 21 with the converted data obtained from the blockchain 23.

[0074] The blockchain 23 is a P2P database consisting of multiple information processing devices connected to a network and functioning as nodes. The blockchain 23 stores NFTs provided by the mobile terminal 21 and provides the stored NFTs to the confirmation terminal 22.

[0075] <Storing NFTs> A specific example of storing NFTs in the blockchain 23 will be described with reference to FIG. 2 .

[0076] For example, assume that time-series behavior history data is generated in the mobile terminal 21, as indicated by an arrow Q11 in FIG.

[0077] In this example, data consisting of location information, user information, RF fingerprint, biometric information, acquisition time of location information, etc. acquired by the mobile terminal 21 at each time within a period T11 of a predetermined length, such as several hours, is generated as one data block of behavioral history data.

[0078] In the mobile terminal 21, behavioral history data is generated in chronological order in units of data blocks and stored in the storage unit 35. In addition, in the mobile terminal 21, a conversion process is performed on the behavioral history data, and the behavioral history data after the conversion process is stored in the blockchain 23.

[0079] Specifically, for example, as the conversion process, a hashing process using a hash function is performed, and a hash value of the behavior history data is calculated as the converted data.

[0080] Since the hash value of the behavior history data has a smaller amount of data than the behavior history data before hashing, performing hashing as a conversion process can also reduce the amount of data. In other words, the amount of data stored in the blockchain 23 can be reduced.

[0081] After the hashing process is performed on the mobile terminal 21, an NFT containing the hash value obtained by the hashing process is generated. The generated NFT is then stored in the blockchain 23 on a container-by-container basis, as indicated by arrow Q12.

[0082] Here, one rectangle drawn in the portion indicated by the arrow Q12 represents one container, such as container C11.

[0083] One container is, for example, one transaction for storing one data block of NFT (behavioral history data) in the blockchain 23.

[0084] For example, one container (transaction) stores not only one data block's worth of NFTs, but also the hash value of the container immediately preceding that container, i.e., the hash value of all data contained in the container stored immediately before. Here, the immediately preceding container is the container that was stored last (immediately before) among the containers already stored in the blockchain 23.

[0085] A container also stores an identifier that uniquely identifies the container. For example, if the container is a transaction, the transaction stores a transaction number, which is an identifier that uniquely identifies the transaction.

[0086] The hash value of the immediately preceding container may be stored in the NFT, or may be stored in a container (transaction) separate from the NFT. In the following, the explanation will be continued assuming that the hash value of the immediately preceding container is stored in the NFT.

[0087] Since each container contains the hash value of the container immediately preceding it, multiple containers are stored in the blockchain 23 in a chain-like manner linked by their hash values.

[0088] Although it is possible to prevent container tampering by simply storing the container in the blockchain 23, container tampering can be further prevented by linking multiple containers in a chain and storing them in the blockchain 23. This is because, similar to the prevention of block tampering in the blockchain 23, if one container is tampered with, the hash value of the tampered container will no longer match the hash value of the container stored in the container immediately following that container.

[0089] In the information processing system 11, a container (transaction) including an NFT is stored in the blockchain 23, so it can be said that the NFT-converted behavioral history data is stored in the blockchain 23.

[0090] In addition, when verifying the authenticity of behavior history data (data to be certified) at the confirmation terminal 22, the confirmation terminal 22 acquires behavior history data from the mobile terminal 21, as shown by arrow Q13, and performs conversion processing on the acquired behavior history data in data block units.

[0091] Here, hashing is performed as the conversion process, and a hash value of the behavior history data is obtained as converted data.

[0092] The verification terminal 22 verifies whether the hash value obtained in this way matches the hash value as converted data included in the NFT in the container read from the blockchain 23, as shown by arrow Q14, for example. In other words, these hash values ​​are compared.

[0093] In this case, if the comparison result shows that the hash values ​​match for a specified data block, it means that the behavioral history data for that data block has not been tampered with, and the authenticity of the behavioral history data (data to be certified) is proven.

[0094] <Description of Data Storage Process> Next, the operation of the information processing system 11 will be described.

[0095] First, the data storage process performed by the mobile terminal 21 will be described with reference to the flowchart of FIG.

[0096] In step S11, the location information acquisition unit 32 acquires location information indicating the current location of the mobile terminal 21 as certification target data.

[0097] In addition, the location information acquisition unit 32 appropriately acquires acquisition time information indicating the time when the location information was acquired, and also acquires user information indicating the user operating (using) the mobile terminal 21 as certification target data.

[0098] For example, the user information may be information indicating a user account in an application that uses the location information acquired in step S11, and the location information acquisition unit 32 acquires the user information using the application program, etc. In addition, if the mobile terminal 21 is a device that can be worn by a user, the user information may also be considered to be wearer information that indicates the user who is wearing the mobile terminal 21.

[0099] Additionally, owner information indicating the owner of the mobile terminal 21 may be acquired as the certification target data.

[0100] In step S12, the biometric information acquisition unit 34 acquires biometric information of the user operating (using) the mobile terminal 21 as certification data, and links the acquired biometric information with data to be certified, such as location information.

[0101] For example, the biometric information acquisition unit 34 captures a part of the user, such as the face of the user operating the mobile terminal 21, as a subject, and acquires, from the resulting image data, information representing the user's biometric characteristics, such as facial features, fingerprint features, iris features, and vein shape patterns, as biometric information. In addition, the biometric information acquisition unit 34 may acquire the user's pulse, voiceprint, and the like as biometric information.

[0102] In this way, by obtaining biometric information representing the biometric characteristics of the user currently operating the mobile terminal 21 as certification data, it is possible to guarantee (prove) which user's data the data to be certified belongs to, thereby improving the authenticity of the data to be certified.

[0103] Moreover, because biometric information is more difficult to tamper with than certification target data, acquiring the biometric information simultaneously or nearly simultaneously with acquiring the certification target data and immediately linking (associating) the certification target data with the biometric information can prevent tampering with the certification target data. In particular, such linking can prevent tampering even before uploading the certification target data (behavioral history data). In other words, it can also prevent tampering with the certification target data within the mobile terminal 21.

[0104] In step S13, the RF fingerprint acquisition unit 33 acquires the RF fingerprint from the communication unit 31 as certification data, and links the acquired RF fingerprint with the data to be certified, such as location information.

[0105] For example, the communication unit 31 of the mobile terminal 21 performs wireless communication with surrounding external devices at regular intervals, or performs wireless communication with surrounding external devices at random timings.

[0106] Therefore, it is highly likely that the mobile terminal 21 is performing wireless communication with a surrounding external device at the time of acquiring the certification target data (location information) or at approximately the same time as the time of acquiring the certification target data.

[0107] Therefore, the RF fingerprint acquisition unit 33 acquires, as certification data, an RF fingerprint representing the characteristics of the wireless signal transmitted from the communication unit 31 to a surrounding external device at the time of acquiring the data to be certified or at a time approximately simultaneous with that acquisition time.

[0108] Here, the wireless communication (wireless signal) in the communication unit 31 that is the target of obtaining the RF fingerprint may be communication related to (associated with) an application that uses the location information, which is the data to be certified, or it may be communication unrelated to the application.

[0109] Furthermore, the external device with which the mobile terminal 21 (communication unit 31) communicates may be any device, such as a base station for connecting the mobile terminal 21 to a network, a wireless LAN (Local Area Network) router based on standards such as Wi-Fi (registered trademark), or a peripheral device such as earphones that perform wireless communication according to standards such as Bluetooth (registered trademark).

[0110] In step S13, an RF fingerprint, which is unique to the mobile terminal 21 that acquired the location information (data to be certified) and can only be obtained at the time the location information is acquired (or at approximately the same time as the acquisition), is acquired as certification data.

[0111] Since RF fingerprints are more difficult to tamper with than the data to be certified, tampering with the data to be certified can be prevented by immediately linking (associating) the data to be certified with the RF fingerprint after obtaining it. In particular, by using RF fingerprints, tampering with the data to be certified (behavioral history data) can be prevented even before it is uploaded.

[0112] In step S14, the data processing unit 36 ​​generates behavior history data including the certification target data such as location information acquired in steps S11 to S13 and linked to each other, and the biometric information and RF fingerprint as certification data.

[0113] For example, the behavior history data includes one data block's worth of location information, acquisition time information, user information, biometric information, and RF fingerprint.

[0114] After generating the behavior history data, the data processing unit 36 ​​supplies the behavior history data to the storage unit 35 for storage.

[0115] Although an example in which biometric information and an RF fingerprint are included as proof data in the storage data (behavioral history data) will be described here, it is sufficient that at least one of the biometric information and the RF fingerprint is included as proof data in the storage data. Alternatively, the proof data may be data different from the biometric information and the RF fingerprint.

[0116] In step S15, the data processing unit 36 ​​performs a conversion process on the behavior history data to generate converted data.

[0117] For example, the data processing unit 36 ​​performs hashing processing using a hash function as the conversion processing, thereby calculating (generating) a hash value of the behavior history data as converted data. Note that the conversion processing is not limited to hashing processing, and may be any processing such as encryption processing.

[0118] By performing the conversion process, the behavioral history data is converted into converted data such as hash values ​​or encrypted data whose contents cannot be understood by humans.

[0119] Therefore, if an NFT containing such converted data is uploaded and stored on the blockchain 23, a third party cannot learn the content of the behavioral history data from the NFT. This makes it possible to prevent the content of the behavioral history data from being leaked. Furthermore, by performing the conversion process, it is possible to reduce the amount of data stored on the blockchain 23 and also to prevent tampering with the behavioral history data after uploading.

[0120] In step S16, the data processing unit 36 ​​generates an NFT including the converted data obtained by the conversion process, i.e., the hash value of the behavior history data.

[0121] More specifically, the data processing unit 36 ​​accesses the blockchain 23 via the communication unit 31 and acquires (reads) the most recently stored container from among the containers (transactions) previously stored in the blockchain 23 by the mobile terminal 21. In other words, the container immediately before the container to be stored is acquired from the blockchain 23.

[0122] The data processing unit 36 ​​performs hashing processing on the acquired container using a hash function to calculate a hash value of the container.

[0123] The data processing unit 36 ​​then generates an NFT containing the hash value of the container and the hash value (converted data) of the behavior history data, and generates a transaction containing the NFT and the transaction number as a container, instructing storage of the NFT. This converts the behavior history data into an NFT.

[0124] In step S17, the communication unit 31 supplies the NFT generated in step S16, more specifically, the transaction including the NFT, to a node (smart contract) that constitutes the blockchain 23, and stores the transaction in the blockchain 23. In other words, the NFT is uploaded to and stored in the blockchain 23.

[0125] When a transaction is supplied from the mobile terminal 21, the node appropriately verifies the transaction, etc., and then stores the transaction (NFT) on the blockchain 23. As a result, the data to be certified, such as location information, and the proof data, such as biometric information, are associated and stored on the blockchain 23. More specifically, the data to be certified and the proof data are associated and hashed, and the resulting hash value is stored on the blockchain 23 as part of the information of the NFT.

[0126] By converting behavioral history data into NFTs and uploading and storing it on the blockchain 23, it is possible to prevent tampering with the behavioral history data after uploading. In particular, by storing the hash value of the immediately preceding transaction in a transaction (container) and linking multiple transactions (containers) in a chain, it is possible to further prevent tampering with the behavioral history data (NFT).

[0127] Once the NFT, i.e., the transaction (container), is stored in the blockchain 23, the data storage process is completed.

[0128] In this way, the mobile terminal 21 acquires and associates the data to be certified with proof data that is related to the data to be certified and is more difficult to tamper with, generates an NFT, and stores the NFT in the blockchain 23.

[0129] In this way, it is possible to prevent tampering with the certification target data, i.e., the behavior history data that is the data to be stored. In other words, it is possible to obtain more reliable certification target data (behavior history data).

[0130] <Explanation of data provision processing and data matching processing> Once the NFT is stored in the blockchain 23, it becomes possible to verify the authenticity of the behavioral history data (data to be certified) stored in the mobile terminal 21 at any time thereafter.

[0131] Hereinafter, the process performed when verifying the authenticity (genuineness) of behavior history data will be described with reference to the flowchart in Fig. 4. That is, the data providing process by the mobile terminal 21 and the data matching process by the confirmation terminal 22 will be described with reference to the flowchart in Fig. 4.

[0132] In the mobile terminal 21, the data provision process is started when the confirmation terminal 22 requests the provision of behavior history data.

[0133] When the data providing process is started, in step S41 the data processing unit 36 ​​reads out the action history data requested by the confirmation terminal 22 from the storage unit 35 and supplies the data to the communication unit 31 .

[0134] For example, in step S41, one or more data blocks of behavior history data are read. In addition, for example, the data processing unit 36 ​​appropriately identifies the transaction number of a transaction (container) in which an NFT containing converted data (hash value) generated from the read behavior history data is stored, and also supplies the identified transaction number to the communication unit 31.

[0135] In step S42, the communication unit 31 transmits the behavior history data supplied from the data processing unit 36 ​​to the confirmation terminal 22 via the network. At this time, the communication unit 31 also transmits the transaction number supplied from the data processing unit 36 ​​to the confirmation terminal 22, as necessary. This means that the behavior history data has been provided from the mobile terminal 21 to the confirmation terminal 22.

[0136] When the behavior history data is transmitted, the data providing process ends.

[0137] When the process of step S42 is performed, the confirmation terminal 22 starts a data matching process.

[0138] When the data matching process is started, in step S61, the communication unit 51 receives (acquires) the behavior history data transmitted from the mobile terminal 21 under the control of the behavior history data acquisition unit 52. The behavior history data acquisition unit 52 supplies the behavior history data received by the communication unit 51 to the data processing unit 53.

[0139] At this time, if a transaction number is also sent along with the behavior history data, the communication unit 51 also receives the transaction number and supplies the transaction number to the data matching unit 54 via the behavior history data acquisition unit 52 .

[0140] In step S62, the data processing unit 53 performs a conversion process on the behavior history data received in step S61.

[0141] For example, the data processing unit 53 performs hashing processing as a conversion process on the behavior history data in units of data blocks, and calculates a hash value as the converted data.

[0142] In step S63, the data matching unit 54 obtains from the blockchain 23 an NFT corresponding to the behavior history data received (obtained) from the mobile terminal 21 in step S61.

[0143] For example, the data matching unit 54 accesses the blockchain 23 via the communication unit 51 and obtains the NFT by reading from the blockchain 23 the transaction indicated by the transaction number appropriately received in step S61.

[0144] The transaction acquired in step S63 stores an NFT corresponding to the behavior history data acquired in step S61. This NFT includes a hash value of the behavior history data.

[0145] In addition, identifying the transaction in which the NFT corresponding to the behavioral history data is stored may be done by any other method, not limited to obtaining a transaction number from the mobile terminal 21.

[0146] In step S64, the data matching unit 54 matches the hash value of the behavior history data included in the NFT acquired in step S63 with the hash value (converted data) of the behavior history data obtained in step S62.

[0147] That is, the data matching unit 54 compares these hash values, and if the hash values ​​match, it determines that the behavior history data provided (supplied) from the mobile terminal 21, i.e., the data to be certified, has not been tampered with. In other words, it determines that a verification result (matching result) has been obtained that proves the authenticity (genuineness) of the behavior history data.

[0148] Once the hash values ​​are verified, the data verification process ends.

[0149] In this way, the mobile terminal 21 provides the behavioral history data to the confirmation terminal 22, and the confirmation terminal 22 compares the hash value based on the provided behavioral history data with the hash value contained in the NFT obtained from the blockchain 23 to verify the authenticity.

[0150] In this way, it is possible to verify whether the behavior history data has been tampered with, and more reliable behavior history data can be obtained. In particular, since the behavior history data also includes biometric information as proof data, it is possible to prove which user the behavior history data belongs to.

[0151] Second Embodiment Description of Data Storage Processing In the above, an example has been described in which proof target data and proof data are associated with each other to create storage data, and an NFT including a hash value of the storage data is generated.

[0152] However, the present invention is not limited to this, and the data to be certified and the proof data may be associated with each other to generate data to be stored only when specific conditions are met. In other words, the data to be certified and the proof data may be associated with each other and stored in the blockchain 23 only when specific conditions are met.

[0153] In this case, when a specific condition is not satisfied, data to be stored is generated that includes the certification target data but does not include the certification data. In other words, data to be stored is generated without associating the certification target data with the certification data.

[0154] As a specific example, the case where a specific condition is satisfied is when the data to be certified is easily falsified or when the certification data is obtainable.

[0155] An example of generating storage data depending on whether the certification target data is easily tampered with will be described below. In such a case, the mobile terminal 21 performs a data storage process shown in FIG.

[0156] In step S91, the mobile terminal 21 acquires data to be certified. For example, in step S91, the location information acquisition unit 32 or the like acquires any data as the data to be certified, such as location information, user information, acquisition time information, image data, owner information, etc.

[0157] In step S92, the data processing unit 36 ​​determines whether the certification target data acquired in step S91 can be easily tampered with, that is, whether a specific condition related to the certification target data is satisfied.

[0158] For example, the data processing unit 36 ​​determines whether the certification target data is easily tampered with based on the data type of the certification target data. Specifically, for example, if the data type of the certification target data is a predetermined specific data type, it is determined that the certification target data is easily tampered with.

[0159] As an example, data types that are considered to be easy to tamper with are types of data that can be easily edited or deleted by the user within the mobile terminal 21, such as location information, user information, acquisition time information, image data of still images and moving images, and owner information.

[0160] If it is determined in step S92 that the data is easily tampered with, the mobile terminal 21 acquires proof data in step S93.

[0161] For example, in step S93, biometric information is acquired by the biometric information acquisition unit 34 in the same manner as in step S12 of FIG. 3, or an RF fingerprint is acquired by the RF fingerprint acquisition unit 33 in the same manner as in step S13 of FIG.

[0162] When the biometric information or RF fingerprint is acquired as certification data, the acquired certification data is immediately linked (associated) with the certification target data. After the certification data is acquired, the process proceeds to step S94.

[0163] On the other hand, if it is determined in step S92 that tampering is not easy, the authenticity of the storage data (data to be certified) can be sufficiently guaranteed even without the certification data, so the processing of step S93 is not performed and the processing then proceeds to step S94.

[0164] Once the process of step S93 has been performed or it is determined in step S92 that the data is not easily tampered with, the data processing unit 36 ​​generates data for storage in step S94.

[0165] For example, when the process of step S93 is performed, the data processing unit 36 ​​generates storage data including the certification target data and certification data acquired and linked in steps S91 and S93. Specifically, behavior history data including, for example, location information and the like as certification target data is generated as storage data.

[0166] On the other hand, if the process of step S93 is not performed, the data processing unit 36 ​​generates storage data that includes the certification target data acquired in step S91 but does not include the certification data.

[0167] After generating the storage data, the data processing unit 36 ​​supplies the storage data to the storage unit 35 for storage.

[0168] Once the data to be stored is generated, steps S95 to S97 are performed and the data storage process is completed. However, since these steps are similar to steps S15 to S17 in FIG. 3, their description will be omitted.

[0169] In this way, the mobile terminal 21 acquires the certification data and generates the storage data as appropriate, depending on whether the certification target data is easily tampered with. In this way, it is possible to appropriately reduce the amount of processing and data while ensuring the credibility (authenticity).

[0170] Even when the data storage process described with reference to FIG. 5 is performed, the information processing system 11 performs processes similar to the data providing process and data matching process described with reference to FIG.

[0171] Even if it is deemed easy to tamper with the data to be certified, it is possible to generate storage data that does not include the certification data when certain conditions are met, such as when certification data cannot be obtained or when approval is obtained from the user or the recipient of the storage data.

[0172] In other words, even if a specific condition is met that the data to be certified is easy to tamper with, if other conditions different from that specific condition are met, the data to be certified may be stored in the blockchain 23 without being associated with the certification data.

[0173] In addition, examples of conditions under which storage data including proof data may be generated include when it is easy to tamper with, when proof data can be obtained, when approval is obtained from a user, etc., when the setting is such that proof data is to be obtained by a user, etc., and when the storage data is used for a predetermined purpose.

[0174] Furthermore, for example, if it is determined that the acquired data to be certified itself has low credibility, storage data including certification data may be generated, storage data including the degree of credibility may be generated, or the generation of storage data may be stopped.

[0175] The data processing unit 36 ​​may generate reliability information indicating the degree of credibility of the storage data depending on the data type of the data to be certified and the certification data, the number of types of certification data acquired, etc., and generate storage data including the reliability information.

[0176] Additionally, for example, when the data processing unit 36 ​​performs conversion processing on the storage data, the type of conversion processing to be performed may be switched depending on the data type of the data to be certified and the storage destination of the storage data. In other words, the conversion processing may be performed in a manner selected from a plurality of different conversion processing methods depending on conditions related to the data to be certified, such as the data type. In this case, the selection result of the conversion processing is notified to the verification terminal 22 as appropriate.

[0177] As an example, when storage data is stored in the mobile terminal 21 and provided to the verification terminal 22, an irreversible hashing process may be performed as the conversion process. In this case, it is difficult to obtain the storage data from the NFT stored in the blockchain 23, thereby improving security.

[0178] The upload destination of the storage data, i.e., the user of the storage data, may specify the conversion process to be performed on the storage data, the type of proof data to be stored in the storage data, whether proof data is required (presence or absence) in the storage data, etc. Since it is conceivable that the amount of storage data will become so large that it will not be possible to store all of the storage data within the mobile terminal 21, the user may be allowed to select the destination to save the storage data, such as within the mobile terminal 21 or an external device.

[0179] Furthermore, it is also possible to prevent the conversion process from being performed depending on whether or not a condition regarding the certification target data is satisfied, such as whether the data type of the certification target data is a predetermined data type. That is, the data processing unit 36 ​​may be configured to switch (decide) whether or not to perform the conversion process on the storage data depending on a condition regarding the certification target data, such as the data type.

[0180] Specifically, for example, if the data type of the certification target data, that is, the content of the certification target data, is a content (data type) that may be made public to others, it is conceivable to prevent the conversion process from being performed.

[0181] <Example of Computer Configuration> The above-described series of processes can be executed by hardware or software. When the series of processes is executed by software, the programs constituting the software are installed on a computer. Here, the computer includes a computer built into dedicated hardware, and a general-purpose personal computer, for example, that can execute various functions by installing various programs.

[0182] FIG. 6 is a block diagram showing an example of the hardware configuration of a computer that executes the above-described series of processes by a program.

[0183] In the computer, a CPU (Central Processing Unit) 501 , a ROM (Read Only Memory) 502 , and a RAM (Random Access Memory) 503 are interconnected by a bus 504 .

[0184] An input / output interface 505 is further connected to the bus 504. An input unit 506, an output unit 507, a storage unit 508, a communication unit 509, and a drive 510 are connected to the input / output interface 505.

[0185] The input unit 506 includes a keyboard, a mouse, a microphone, an image sensor, etc. The output unit 507 includes a display, a speaker, etc. The storage unit 508 includes a hard disk, a non-volatile memory, etc. The communication unit 509 includes a network interface, etc. The drive 510 drives a removable storage medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory.

[0186] In a computer configured as described above, the CPU 501 performs the above-described series of processes by, for example, loading a program stored in the memory unit 508 into the RAM 503 via the input / output interface 505 and the bus 504 and executing it.

[0187] The program executed by the computer (CPU 501) can be provided by being stored in, for example, a removable storage medium 511 such as a package medium. The program can also be provided via a wired or wireless transmission medium such as a local area network, the Internet, or digital satellite broadcasting.

[0188] In a computer, a program can be installed in the storage unit 508 via the input / output interface 505 by inserting a removable storage medium 511 into the drive 510. The program can also be received by the communication unit 509 via a wired or wireless transmission medium and installed in the storage unit 508. Alternatively, the program can be installed in the ROM 502 or the storage unit 508 in advance.

[0189] The program executed by the computer may be a program that processes in chronological order according to the order described in this specification, or may be a program that processes in parallel or at the required timing, such as when called.

[0190] Furthermore, the embodiments of the present technology are not limited to the above-described embodiments, and various modifications are possible within the scope of the gist of the present technology.

[0191] For example, the present technology can be configured as a cloud computing system in which a single function is shared and processed collaboratively by a plurality of devices via a network.

[0192] Furthermore, each step described in the above flowchart can be executed by one device, or can be shared and executed by a plurality of devices.

[0193] Furthermore, when one step includes multiple processes, the multiple processes included in that one step can be executed by one device or can be shared and executed by multiple devices.

[0194] Furthermore, the present technology can also be configured as follows.

[0195] (1) An information processing method including a step in which an information processing system associates second data, which is related to first data and is more difficult to tamper with than the first data, with the first data and stores the second data in a P2P database. (2) The information processing method described in (1), in which non-fungible data is generated based on the first data and the second data, and the non-fungible data is stored in the P2P database. (3) The information processing method described in (2), in which the non-fungible data is an NFT. (4) The information processing method described in (2) or (3), in which a conversion process is performed on storage data including the first data and the second data, and the non-fungible data includes data obtained by the conversion process. (5) The information processing method described in (4), in which the conversion process is a hashing process or an encryption process. (6) The information processing method described in (4) or (5), in which the conversion process selected from a plurality of the conversion processes is performed depending on a condition related to the first data. (7) The information processing method according to (4) or (5), wherein whether or not to perform the conversion process is switched depending on a condition related to the first data. (8) The information processing method according to any one of (1) to (7), wherein the second data is at least one of biometric information and an RF fingerprint. (9) The information processing method according to any one of (1) to (8), wherein, if a specific condition is satisfied, the second data and the first data are associated with each other and stored in the P2P database. (10) The information processing method according to (9), wherein the specific condition is satisfied if the first data is susceptible to tampering. (11) The information processing method according to (10), wherein, based on the type of the first data, it is determined whether the first data is susceptible to tampering. (12) The information processing method according to (11), wherein, if the first data is location information, owner information, user information, or image data, it is determined that the first data is susceptible to tampering.(13) The information processing method according to any one of (9) to (12), wherein, when the specific condition is satisfied, if another condition different from the specific condition is satisfied, the first data is stored in the P2P database without being associated with the second data. (14) The information processing method according to any one of (9) to (13), wherein, when the specific condition is not satisfied, the first data is stored in the P2P database without being associated with the second data. (15) The information processing method according to (3), wherein a transaction including the NFT is stored in the P2P database, and the transaction includes a hash value of another transaction including another NFT that was stored in the P2P database immediately before the transaction. (16) An information processing method including: an information processing system; acquiring storage data including the first data and second data that is related to the first data and is more difficult to tamper with than the first data; and comparing the acquired storage data with data corresponding to the storage data stored in the P2P database. (17) The information processing method according to (16), further comprising: performing a conversion process on the storage data; and comparing the converted data obtained by the conversion process with the converted data obtained from the P2P database as data corresponding to the storage data. (18) The information processing method according to (17), wherein the conversion process is a hashing process. (19) The information processing method according to (17) or (18), wherein non-fungible data including the converted data is obtained from the P2P database. (20) The information processing method according to (19), wherein the non-fungible data is an NFT. (21) The information processing method according to any one of (16) to (20), wherein the second data is at least one of biometric information and an RF fingerprint. (22) The information processing method according to (20), wherein a transaction including the NFT is obtained from the P2P database; and wherein the transaction includes a hash value of another transaction including another NFT that was stored in the P2P database immediately before the transaction.(23) An information processing system comprising a first information processing device and a second information processing device, wherein the first information processing device comprises: a data processing unit that generates storage data including second data that is related to first data and is more difficult to tamper with than the first data, and the first data, and stores data corresponding to the storage data in a P2P database; and a first communication unit that transmits the storage data to the second information processing device, and the second information processing device comprises: a second communication unit that receives the storage data transmitted by the first information processing device, and a data matching unit that matches the storage data received by the second communication unit with data corresponding to the storage data obtained from the P2P database.

[0196] REFERENCE SIGNS LIST 11 Information processing system, 21 Mobile terminal, 22 Verification terminal, 23 Blockchain, 31 Communication unit, 32 Location information acquisition unit, 33 RF fingerprint acquisition unit, 34 Biometric information acquisition unit, 35 Storage unit, 36 Data processing unit, 51 Communication unit, 52 Behavioral history data acquisition unit, 53 Data processing unit, 54 Data matching unit

Claims

1. An information processing method including a step in which an information processing system associates second data, which is related to first data and is more difficult to tamper with than the first data, with the first data and stores the second data in a P2P database.

2. The information processing method according to claim 1, further comprising generating non-fungible data based on said first data and said second data, and storing said non-fungible data in said P2P database.

3. The information processing method according to claim 2, wherein the non-fungible data is an NFT.

4. The information processing method according to claim 2, further comprising the steps of: performing a conversion process on storage data including the first data and the second data; and generating the irreplaceable data including data obtained by the conversion process.

5. The information processing method according to claim 4, wherein the conversion process is a hashing process or an encryption process.

6. The information processing method according to claim 4, further comprising the step of: selecting one of a plurality of said conversion processes in accordance with a condition relating to said first data; 7. The information processing method according to claim 4, further comprising switching whether or not to perform the conversion process depending on a condition related to the first data.

8. The information processing method according to claim 1, wherein the second data is at least one of biometric information and an RF fingerprint.

9. The information processing method according to claim 1, further comprising the steps of: if a specific condition is satisfied, storing the second data and the first data in association with each other in the P2P database.

10. The information processing method according to claim 9, wherein the specific condition is satisfied if the first data is easily tampered with.

11. The information processing method according to claim 10, further comprising determining whether or not the first data is easily tampered with based on the type of the first data.

12. The information processing method according to claim 11, wherein, when the first data is location information, owner information, user information, or image data, the first data is determined to be easily tampered with.

13. The information processing method according to claim 9, further comprising: when the specific condition is satisfied, and another condition different from the specific condition is satisfied, storing the first data in the P2P database without associating it with the second data.

14. The information processing method according to claim 9, further comprising storing the first data in the P2P database without associating it with the second data if the particular condition is not met.

15. The information processing method of claim 3, further comprising storing a transaction including the NFT in the P2P database, the transaction including a hash value of another transaction including another NFT that was stored in the P2P database immediately before the transaction.

16. An information processing method including a step in which an information processing system acquires storage data including first data and second data that is related to first data and is more difficult to tamper with than the first data, and the first data, and compares the acquired storage data with data that corresponds to the storage data and is stored in a P2P database.

17. The information processing method according to claim 16, further comprising: performing a conversion process on the data to be stored; and comparing the converted data obtained by the conversion process with the converted data obtained from the P2P database as data corresponding to the data to be stored.

18. The information processing method according to claim 17, wherein the conversion process is a hashing process.

19. The information processing method according to claim 16, wherein the second data is at least one of biometric information and an RF fingerprint.

20. An information processing system comprising a first information processing device and a second information processing device, wherein the first information processing device comprises: a data processing unit that generates storage data including second data that is related to first data and is more difficult to tamper with than the first data, and the first data, and stores data corresponding to the storage data in a P2P database; and a first communication unit that transmits the storage data to the second information processing device, and the second information processing device comprises: a second communication unit that receives the storage data transmitted by the first information processing device, and a data matching unit that matches the storage data received by the second communication unit with data corresponding to the storage data obtained from the P2P database.

Images