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JP-7856794-B2 - On-device-based personal data leakage prevention and personalization response systems and methods

JP7856794B2JP 7856794 B2JP7856794 B2JP 7856794B2JP-7856794-B2

Inventors

  • ユン，ソン ノ
  • ユ，サン ウォン

Assignees

  • ソウル ナショナル ユニヴァーシティ アール アンド ディービー ファウンデーション

Dates

Publication Date
20260511
Application Date
20231012
Priority Date
20230823

Claims (7)

  1. In preventing on-device-based personal data leaks and in personalized response systems, Personal Identifiable Information (PII) is detected from the user's questions entered. The system includes multiple user terminals that transfer user-neutral questions obtained by converting the PII into neutral information, and a management server that receives the user-neutral questions and trains a management language model that generates a common response pattern for each neutral question pattern . The aforementioned user terminal is A response generation model that understands the context of the user's question and generates a response through natural language processing analysis, and The PII detection model includes a method for generating user-neutral questions by masking or converting the PII extracted from the user questions into predetermined words. The response generation model is a language model that learns user question patterns using the user questions, A personalized response system in which the user question patterns are learned for each same context based on a language distribution including the frequency and type of words in the user questions .
  2. The aforementioned PII detection model is Based on the initial PII pre-stored in the PII database, it is determined whether the individual characters or words constituting the user question constitute personal information, The personalized response system according to claim 1 , wherein the PII is extracted from the user question based on named entity recognition (NER) and stored in the PII database.
  3. In preventing on-device-based personal data leaks and in personalized response systems, Personal Identifiable Information (PII) is detected from the user's questions entered. Multiple user terminals that transfer user-neutral questions obtained by converting the aforementioned PII into neutral information, and The system includes a management server that receives the user-neutral questions and trains a management language model that generates a common response pattern for each neutral question pattern, The management language model learns a PII pattern for each neutral question pattern by utilizing the position of the neutral information in the sentence structure for the user-neutral question, and is a personalized response system.
  4. The personalized response system according to claim 1 or 3 , wherein the PII is divided into direct identifiers and quasi-identifiers that can identify a specific individual.
  5. On-device-based prevention of personal information leaks and personalized responses on user terminals, Communication module, The system includes a memory in which a personalized response program is stored, and a processor that executes the personalized response program. The personalized response program detects PII from the input user question, converts the PII into neutral information, and transfers the user-neutral question converted into neutral information to the management server . The aforementioned personalized response program is A response generation model that understands the context of the user's question and generates a response through natural language processing analysis, and This includes a PII detection model that generates user-neutral questions by masking or converting the PII extracted from the user questions into predetermined words. The response generation model is a language model that learns user question patterns using the user questions, The user terminal is one in which the user question patterns are learned for each same context based on a language distribution that includes the frequency and type of words in the user questions .
  6. The aforementioned PII detection model is Based on the initial PII pre-stored in the PII database, it is determined whether the individual characters or words constituting the user question constitute personal information, The user terminal according to claim 5 , which extracts the PII from the user question based on named entity recognition (NER) and stores it in the PII database.
  7. In preventing on-device-based personal information leaks and in personal response methods performed by personal response systems, (a) The management server receives user-neutral questions from multiple user terminals in which PII (Personal Identifiable Information) contained in the user questions has been converted into neutral information; and (b) The management server receives the user-neutral questions and trains a management language model that generates a common response pattern for each neutral question pattern . The management language model learns a PII pattern for each neutral question pattern by utilizing the position of the neutral information in the sentence structure for the user-neutral question, and is a personalized response method.

Description

This invention relates to an on-device-based system and method for preventing the leakage of personal information and for providing personalized responses. Recently, with the significant attention given to generative conversational artificial intelligence (AI) like ChatGPT, concerns about the leakage of personally identifiable information (PII) and the limitations of AI, such as the lack of personalization capabilities, have become apparent. In existing smartphones, a small amount of PII (Personal Information Indicator), such as fingerprints and facial recognition data, is not sent to a management server but processed within the user's device. However, with the recently emerged generative conversational AI, the amount of PII that needs to be stored is far greater. Models like ChatGPT are emerging that can handle questions and answers not only through text but also through images and videos, which have much larger data volumes. As users use their personal devices for extended periods, the amount of information accumulated increases even further, highlighting the limitations of existing systems that manage only a small portion of PII on the device itself. On the other hand, while personalizing conversational AI is absolutely necessary to provide responses tailored to user tendencies, there is a problem in that this process must be complemented by technologies to prevent the leakage of PII (Personal Information Indicator). Therefore, to solve the aforementioned problems, the present invention proposes an artificial intelligence agent that operates on a user terminal such as a smartphone, without transmitting PII to a management server such as a cloud. This is a diagram illustrating the configuration of an on-device-based personal information leakage prevention and personal response system according to one embodiment of the present invention.This diagram illustrates an on-device-based personal information leakage prevention and personalized response system according to one embodiment of the present invention.This figure illustrates an example of PII according to one embodiment of the present invention.This diagram shows the configuration of an on-device-based personal information leakage prevention and a personalized response user terminal according to another embodiment of the present invention.This is a flowchart illustrating a method for preventing the leakage of personal information and providing personalized responses, as performed on a user terminal according to another embodiment of the present invention.This figure illustrates a personalized response program executed on a user terminal according to another embodiment of the present invention.This figure illustrates the data interaction between a management server and a user terminal according to another embodiment of the present invention.This flowchart illustrates another embodiment of the present invention that demonstrates on-device-based prevention of personal information leakage and a personalized response method performed by a personalized response system. Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, so that they can be easily implemented by a person with ordinary skill in the art to which the present invention pertains. However, the present invention can be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly illustrate the present invention with the drawings, parts unrelated to the description have been omitted, and similar parts throughout the specification are denoted by similar reference numerals. Throughout the specification, "connected" to another part includes not only "direct connection" but also "electrical connection" with other elements in between. Furthermore, "contains" a component, unless otherwise stated, means that it may contain other components rather than excluding them. In this specification, “Unit” includes units implemented by hardware, units implemented by software, and units implemented using both. Furthermore, one unit may be implemented using two or more hardware components, and two or more units may be implemented by one hardware component. On the other hand, “Unit” is not limited to software or hardware; it may also be configured to reside in an addressable storage medium, or to regenerate one or more processors. Therefore, as an example, “Unit” includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. Components and the functions provided within “Unit” can be combined with fewer components and “Unit” or further separated into additional components and “Unit”. Moreover, components and “Unit” can be embodied t