# US 12,008,078 B2 · Concept for anonymous re-identification > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,008,078 B2 - Document ID: US12008078B2 - Status: Granted - Kind: B2 - Application number: 17/446,175 - Filed: 2021-08-27 - Published: 2022-03-24 - Granted: 2024-06-11 - Earliest claimed: 2020-09-22 - Assignees: Grazper Technologies ApS - Applicants: Grazper Technologies ApS - Inventors: Thomas Jakobsen - Web page: https://patentgrep.com/patent/US12008078B2 ## CPC - G06F 18/22: Matching criteria, e.g. proximity measures - H04W 12/00: Security arrangements; Authentication; Protecting privacy or anonymity - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06F 18/2413: based on distances to training or reference patterns - G06F 21/602: {Providing cryptographic facilities or services} - G06T 9/00: Image coding (bandwidth or redundancy reduction for static pictures H04N1/41; coding or decoding of static colour picture signals H04N1/64; methods or arrangements for coding, decoding, compressing or decompressing digital video signals H04N19/00) - G06V 10/48: by mapping characteristic values of the pattern into a parameter space, e.g. Hough transformation - G06V 10/74: Image or video pattern matching; Proximity measures in feature spaces - G06V 10/764: using classification, e.g. of video objects - G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting - G06V 20/40: in video content (extracting overlay text G06V20/62; video retrieval G06F16/70; processing of video elementary streams in video servers H04N21/234; processing of video elementary streams in video clients H04N21/44) - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - H04L 9/0643: {Hash functions, e.g. MD5, SHA, HMAC or f9 MAC} - H04L 9/0662: {with particular pseudorandom sequence generator} - H04L 9/0872: {using geo-location information, e.g. location data, time, relative position or proximity to other entities} - H04L 9/0894: {Escrow, recovery or storing of secret information, e.g. secret key escrow or cryptographic key storage} - H04W 12/02: Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII] - G06N 20/00: Machine learning - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Examples relate to a concept for anonymous re-identification, more specifically, but not exclusively, to systems, apparatuses, methods and computer programs for performing anonymous re-identification and for training a machine-learning model for use in anonymous re-identification. An apparatus for re-identification comprises processing circuitry configured to obtain media data via an interface. the processing circuitry is configured to generate a re-identification code representing at least a portion of the media data using a hashing algorithm. The processing circuitry is configured to transform the re-identification code using a transformation functionality to obtain a transformed re-identification code. The transformation functionality is configured to transform the re-identification code such that, if the re-identification code is similar to a further re-identification code generated by the hashing algorithm according to a similarity metric, the transformed re-identification code is similar to a further transformed re-identification code being a transformed version of the further re-identification code. The transformation functionality is configured to transform the re-identification code based on a transformation parameter, with the transformation parameter being dependent on a time and/or a location. The processing circuitry is configured to provide the transformed re-identification code. ## Claims ### Claim 1 An apparatus for re-identification, the apparatus comprising processing circuitry configured to: obtain media data via an interface; generate a re-identification code representing at least a portion of the media data using a hashing algorithm; transform the re-identification code using a transformation functionality to obtain a transformed re-identification code, wherein the transformation functionality is configured to transform the re-identification code such that, if the re-identification code is similar to a further re-identification code generated by the hashing algorithm according to a similarity metric, the transformed re-identification code is similar to a further transformed re-identification code being a transformed version of the further re-identification code, wherein the transformation functionality is configured to transform the re-identification code based on a transformation parameter, the transformation parameter being dependent on a time, wherein the processing circuitry is configured to generate two transformation parameters for two points in time, and to gradually adapt the transformation parameter according to a pre-defined time schedule, the adaptation based on a linear interpolation between the two transformation parameters in between the two points in time; and provide the transformed re-identification code. ### Claim 2 (depends on 1) The apparatus according to claim 1, wherein the processing circuitry is configured to delete a previously used transformation parameter after adapting the transformation parameter. ### Claim 3 (depends on 1) The apparatus according to claim 1, wherein the transformation parameter is derived from a cryptographic secret. ### Claim 4 (depends on 1) The apparatus according to claim 1, wherein the transformation functionality is configured to perform a non-linear transformation of the re-identification code. ### Claim 5 (depends on 4) The apparatus according to claim 4, wherein the transformation functionality is configured to perform the non-linear transformation using a machine-learning model. ### Claim 6 (depends on 1) The apparatus according to claim 1, wherein the media data is one of image data, video data, audio data, a three-dimensional representation of movement of an object and text-based media data. ### Claim 7 (depends on 1) The apparatus according to claim 1, wherein the processing circuitry is configured to obtain the media data from an imaging sensor of a camera, the media data being one of image data and video data, or wherein the processing circuitry is configured to obtain the media data from one or more media data generation devices via a network. ### Claim 8 (depends on 7) The apparatus according to claim 7, wherein the processing circuitry is configured to obtain the media data from two or more media data generation devices being located at different locations, wherein the transformation parameter is further dependent on a location, wherein the processing circuitry is configured to perform the transformation using different transformation parameters for the two or more media data generation devices being located at different locations. ### Claim 9 (depends on 8) The apparatus according to claim 8, wherein the processing circuitry is configured to obtain the media data from a first, a second and a third media data generation device being located at different locations, with the second media data generation device being located between the first and the third media data generation device, wherein the processing circuitry is configured to generate the transformation parameter for the media data obtained from the second media data generation device based on a linear interpolation between transformation parameters used for the media data obtained from the first and third media data generation devices. ### Claim 10 (depends on 1) A camera device comprising an imaging sensor and the apparatus according to claim 1. ### Claim 11 (depends on 1) A system comprising two or more apparatuses according to claim 1. ### Claim 12 (depends on 11) The system according to claim 11, wherein the two or more apparatuses are configured to use the same transformation parameter at the same time, or wherein the transformation parameter is dependent on a location, wherein the two or more apparatuses are configured to process media data originating from two or more locations, and to use different transformation parameters for the media data originating from the two or more locations. ### Claim 13 A method for training a machine-learning model, the method comprising: obtaining training data for training the machine-learning model, the training data comprising at least a plurality of re-identification codes; and training the machine-learning model using the training data, the machine-learning model being trained to non-linearly transform the plurality of re-identification codes into a plurality of transformed re-identification codes such that if a re-identification code is similar to a further re-identification code according to a similarity metric, the transformed re-identification code is similar to a further transformed re-identification code being a transformed version of the further re-identification code, wherein the transformation of each re-identification code is based on a transformation parameter, the transformation parameter being dependent on a time, wherein two transformation parameters are generated for two points in time, and the transformation parameter is gradually adaptable according to a pre-defined time schedule, the adaptation based on a linear interpolation between the two transformation parameters in between the two points in time. ### Claim 14 An apparatus for re-identification, the apparatus comprising processing circuitry configured to: obtain media data via a network interface from a first, a second, and a third media data generation device being located at different locations, with the second media data generation device being located between the first and the third media data generation device; generate a re-identification code representing at least a portion of the media data using a hashing algorithm; transform the re-identification code using a transformation functionality to obtain a transformed re-identification code, wherein the transformation functionality is configured to transform the re-identification code such that, if the re-identification code is similar to a further re-identification code generated by the hashing algorithm according to a similarity metric, the transformed re-identification code is similar to a further transformed re-identification code being a transformed version of the further re-identification code, wherein the transformation functionality is configured to transform the re-identification code based on a transformation parameter, the transformation parameter being dependent on a time and/or a location, wherein the processing circuitry is configured to perform the transformation using different transformation parameters for the first, the second, and the third media data generation devices, wherein the processing circuitry is configured to generate the transformation parameter for the media data obtained from the second media data generation device based on a linear interpolation between transformation parameters used for the media data obtained from the first and third media data generation devices; and provide the transformed re-identification code.