Bowen Zhao, Yingjiu Li, Ximeng Liu, HweeHwa Pang, R. Deng
{"title":"FREED:一种有效的个人身份再识别隐私保护方案","authors":"Bowen Zhao, Yingjiu Li, Ximeng Liu, HweeHwa Pang, R. Deng","doi":"10.1109/DSC54232.2022.9888863","DOIUrl":null,"url":null,"abstract":"Person Re-IDentification (Re-ID) is a critical technology to identify a target person from captured person images by surveillance cameras. However, person Re-ID has triggered great concerns of personal image privacy. Although the law (e.g., GDPR) has stipulated person images are personal private data, there is no an efficient solution to tackle the image privacy concern for person Re-ID. To this end, we propose FREED, the first system solution for privacy-preserving person Re-ID, which supports the state-of-the-art person Re-ID operations on encrypted feature vectors of person images. To handle the encryption of feature vectors effectively and enable person Re-ID operations on encrypted feature vectors efficiently, FREED develops a suite of batch secure computing protocols based on a twin-server architecture and the threshold Paillier cryptosystem. We demonstrate our secure computing protocols are more efficient than existing protocols and FREED achieves a precision equal to the state-of-the-art plaintext method.","PeriodicalId":368903,"journal":{"name":"2022 IEEE Conference on Dependable and Secure Computing (DSC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"FREED: An Efficient Privacy-Preserving Solution for Person Re-IDentification\",\"authors\":\"Bowen Zhao, Yingjiu Li, Ximeng Liu, HweeHwa Pang, R. Deng\",\"doi\":\"10.1109/DSC54232.2022.9888863\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Person Re-IDentification (Re-ID) is a critical technology to identify a target person from captured person images by surveillance cameras. However, person Re-ID has triggered great concerns of personal image privacy. Although the law (e.g., GDPR) has stipulated person images are personal private data, there is no an efficient solution to tackle the image privacy concern for person Re-ID. To this end, we propose FREED, the first system solution for privacy-preserving person Re-ID, which supports the state-of-the-art person Re-ID operations on encrypted feature vectors of person images. To handle the encryption of feature vectors effectively and enable person Re-ID operations on encrypted feature vectors efficiently, FREED develops a suite of batch secure computing protocols based on a twin-server architecture and the threshold Paillier cryptosystem. We demonstrate our secure computing protocols are more efficient than existing protocols and FREED achieves a precision equal to the state-of-the-art plaintext method.\",\"PeriodicalId\":368903,\"journal\":{\"name\":\"2022 IEEE Conference on Dependable and Secure Computing (DSC)\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Conference on Dependable and Secure Computing (DSC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DSC54232.2022.9888863\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Conference on Dependable and Secure Computing (DSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSC54232.2022.9888863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
FREED: An Efficient Privacy-Preserving Solution for Person Re-IDentification
Person Re-IDentification (Re-ID) is a critical technology to identify a target person from captured person images by surveillance cameras. However, person Re-ID has triggered great concerns of personal image privacy. Although the law (e.g., GDPR) has stipulated person images are personal private data, there is no an efficient solution to tackle the image privacy concern for person Re-ID. To this end, we propose FREED, the first system solution for privacy-preserving person Re-ID, which supports the state-of-the-art person Re-ID operations on encrypted feature vectors of person images. To handle the encryption of feature vectors effectively and enable person Re-ID operations on encrypted feature vectors efficiently, FREED develops a suite of batch secure computing protocols based on a twin-server architecture and the threshold Paillier cryptosystem. We demonstrate our secure computing protocols are more efficient than existing protocols and FREED achieves a precision equal to the state-of-the-art plaintext method.