{"title":"人工智能正在接管世界吗?不,但这让隐私少了些","authors":"G. Ateniese","doi":"10.1145/3384942.3406872","DOIUrl":null,"url":null,"abstract":"This talk highlights challenges and opportunities for trustworthy AI with a focus on privacy attacks and countermeasures. AI and machine learning have no future if their privacy and security concerns are not addressed. Machine learning models could hide malicious code or back doors, and leak private information about users. We will explore inference attacks against machine learning models and frameworks (e.g., federated learning), and set out the requirements for privacy-preserving AI systems.","PeriodicalId":312816,"journal":{"name":"Proceedings of the 8th International Workshop on Security in Blockchain and Cloud Computing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Is AI Taking Over the World? No, but It's Making it Less Private\",\"authors\":\"G. Ateniese\",\"doi\":\"10.1145/3384942.3406872\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This talk highlights challenges and opportunities for trustworthy AI with a focus on privacy attacks and countermeasures. AI and machine learning have no future if their privacy and security concerns are not addressed. Machine learning models could hide malicious code or back doors, and leak private information about users. We will explore inference attacks against machine learning models and frameworks (e.g., federated learning), and set out the requirements for privacy-preserving AI systems.\",\"PeriodicalId\":312816,\"journal\":{\"name\":\"Proceedings of the 8th International Workshop on Security in Blockchain and Cloud Computing\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 8th International Workshop on Security in Blockchain and Cloud Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3384942.3406872\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 8th International Workshop on Security in Blockchain and Cloud Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3384942.3406872","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Is AI Taking Over the World? No, but It's Making it Less Private
This talk highlights challenges and opportunities for trustworthy AI with a focus on privacy attacks and countermeasures. AI and machine learning have no future if their privacy and security concerns are not addressed. Machine learning models could hide malicious code or back doors, and leak private information about users. We will explore inference attacks against machine learning models and frameworks (e.g., federated learning), and set out the requirements for privacy-preserving AI systems.