Tongzhen Si , Penglei Li , Xiaohui Yang , Linkun Fan , Fazhi He
{"title":"用于人员重新识别的模型感知隐私保护与启动触发方法","authors":"Tongzhen Si , Penglei Li , Xiaohui Yang , Linkun Fan , Fazhi He","doi":"10.1016/j.ipm.2024.103819","DOIUrl":null,"url":null,"abstract":"<div><p>Person Re-identification (ReID) could search for the same pedestrian from non-overlapping cameras, which completes the pedestrian location and search purpose. However, the process contains much sensitive pedestrian information and raises serious privacy problems. Conventional methods mainly remove identity-related features from pedestrian images to alleviate the privacy issue. Unfortunately, these strategies cause pedestrian information loss and poor data utility. In the paper, we propose a novel Model-Aware Privacy-Preserving with Start Trigger (MPST) method, which not only prevents personal identity for third parties but also achieves accurate pedestrian location for authorized organizations. The core idea is that authorized organizations obtain the start trigger to activate the ReID model that has the ability to search for target pedestrians, while third parties (i.e., hackers) cannot employ the ReID model to complete the pedestrian matching task without the start trigger. To this end, we develop a universal adversarial algorithm to learn an ingenious start trigger for the person ReID system. Afterwards, we further design a model-aware training strategy to facilitate our deep model to perceive issued instructions by synthetically utilizing the start trigger and original pedestrian samples. As a result, we successfully install an activate button to change the ReID model state for deciding whether the deep model has the ability to search for pedestrians or not. Abundant experiments demonstrate that the proposed MPST is effective for pedestrian identity anonymization. Our study achieves superior performance for authorized organizations and completes the privacy protection goal.</p></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4000,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Model-aware privacy-preserving with start trigger method for person re-identification\",\"authors\":\"Tongzhen Si , Penglei Li , Xiaohui Yang , Linkun Fan , Fazhi He\",\"doi\":\"10.1016/j.ipm.2024.103819\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Person Re-identification (ReID) could search for the same pedestrian from non-overlapping cameras, which completes the pedestrian location and search purpose. However, the process contains much sensitive pedestrian information and raises serious privacy problems. Conventional methods mainly remove identity-related features from pedestrian images to alleviate the privacy issue. Unfortunately, these strategies cause pedestrian information loss and poor data utility. In the paper, we propose a novel Model-Aware Privacy-Preserving with Start Trigger (MPST) method, which not only prevents personal identity for third parties but also achieves accurate pedestrian location for authorized organizations. The core idea is that authorized organizations obtain the start trigger to activate the ReID model that has the ability to search for target pedestrians, while third parties (i.e., hackers) cannot employ the ReID model to complete the pedestrian matching task without the start trigger. To this end, we develop a universal adversarial algorithm to learn an ingenious start trigger for the person ReID system. Afterwards, we further design a model-aware training strategy to facilitate our deep model to perceive issued instructions by synthetically utilizing the start trigger and original pedestrian samples. As a result, we successfully install an activate button to change the ReID model state for deciding whether the deep model has the ability to search for pedestrians or not. Abundant experiments demonstrate that the proposed MPST is effective for pedestrian identity anonymization. Our study achieves superior performance for authorized organizations and completes the privacy protection goal.</p></div>\",\"PeriodicalId\":50365,\"journal\":{\"name\":\"Information Processing & Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.4000,\"publicationDate\":\"2024-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Processing & Management\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S030645732400178X\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing & Management","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S030645732400178X","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Model-aware privacy-preserving with start trigger method for person re-identification
Person Re-identification (ReID) could search for the same pedestrian from non-overlapping cameras, which completes the pedestrian location and search purpose. However, the process contains much sensitive pedestrian information and raises serious privacy problems. Conventional methods mainly remove identity-related features from pedestrian images to alleviate the privacy issue. Unfortunately, these strategies cause pedestrian information loss and poor data utility. In the paper, we propose a novel Model-Aware Privacy-Preserving with Start Trigger (MPST) method, which not only prevents personal identity for third parties but also achieves accurate pedestrian location for authorized organizations. The core idea is that authorized organizations obtain the start trigger to activate the ReID model that has the ability to search for target pedestrians, while third parties (i.e., hackers) cannot employ the ReID model to complete the pedestrian matching task without the start trigger. To this end, we develop a universal adversarial algorithm to learn an ingenious start trigger for the person ReID system. Afterwards, we further design a model-aware training strategy to facilitate our deep model to perceive issued instructions by synthetically utilizing the start trigger and original pedestrian samples. As a result, we successfully install an activate button to change the ReID model state for deciding whether the deep model has the ability to search for pedestrians or not. Abundant experiments demonstrate that the proposed MPST is effective for pedestrian identity anonymization. Our study achieves superior performance for authorized organizations and completes the privacy protection goal.
期刊介绍:
Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing.
We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.