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\u738b\u82e5\u611a\u662f\u4e9a\u5229\u6851\u90a3\u5dde\u7acb\u5927\u5b66\uff08Arizona State University\uff09\u7684\u4e00\u540d\u52a9\u7406\u6559\u6388\uff0c\u4e3b\u8981\u7814\u7a76\u65b9\u5411\u662f\u4e8c\u8fdb\u5236\u7a0b\u5e8f\u5206\u6790\uff0c\u5305\u62ec\u6f0f\u6d1e\u6316\u6398\u3001\u6f0f\u6d1e\u5229\u7528\u3001\u7a0b\u5e8f\u4fee\u8865\u7b49\u3002\u4ed6\u662f\u4e8c\u8fdb\u5236\u5206\u6790\u5e73\u53f0angr \u7684\u5171\u540c\u521b\u59cb\u4eba\u4e0e\u6838\u5fc3\u5f00\u53d1\u8005\u4e4b\u4e00\u3002\u4ed6\u7684\u8bba\u6587\u66fe\u53d1\u8868\u5728 Security and Privacy\u3001 USENIXSecurity\u3001 CCS\u3001 NDSS \u7b49\u4f1a\u8bae\u4e0a\uff0c\u5e76\u83b7\u5f97\u8fc7\u4e24\u6b21\u4f1a\u8bae\u6700\u4f73\u8bba\u6587\u5956\u3002\u4ed6\u662f\u4e00\u4f4d\u975e\u8457\u540d CTF\u8d5b\u68cd\uff0c \u73b0\u5728\u662f Shellphish \u7684\u961f\u957f\uff0c \u4e13\u653b\u9006\u5411\u5de5\u7a0b\u7c7b\u9898\u76ee\u3002\u4ed6\u8fd8\u66fe\u662f Shellphish CGC \u56e2\u961f\u7684\u6210\u5458\uff0c\u5728 2016 \u5e74 DARPA Cyber Grand Challenge \u51b3\u8d5b\u4e0a\u593a\u5f97\u4e86\u7b2c\u4e09\u540d\u3002
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\"\"<\/figure>\n\n\n\n

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\"\"<\/figure>\n\n\n\n

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\"\"<\/figure>\n\n\n\n

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\"\"<\/figure>\n\n\n\n

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\"\"<\/figure>\n\n\n\n

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\u5e38\u7528ID Pizza, \u6df1\u5733\u5927\u5b66\u672c\u79d1\u5728\u8bfb, Eur3ka&r3kapig\u4e3b\u529b\u9006\u5411\u9009\u624b, \u53c2\u52a0\u8fc7\u591a\u6b21CTF\u5927\u8d5b\u5e76\u53d6\u5f97\u4e00\u5b9a\u6210\u7ee9.
\u5185\u5bb9\u6458\u8981\uff1a<\/strong>
\u4ee5\u4e2a\u4eba\u7ecf\u9a8c\u4ecb\u7ecdCTF\u4e2d\u9006\u5411\u65b9\u5411\u7684\u5165\u95e8\u8def\u7ebf\u4e0e\u5fc5\u5907\u6280\u80fd, \u8bb2\u89e3\u90e8\u5206CTF\u9006\u5411\u9898\u76ee\u4e2d\u5e38\u89c1\u7684\u89e3\u9898\u6280\u5de7, \u6700\u540e\u5206\u4eab\u4e00\u4e9b\u4e2a\u4eba\u5173\u4e8e\u53c2\u52a0CTF\u6bd4\u8d5b\u7684\u7ecf\u9a8c\u4e0e\u60f3\u6cd5\u3002<\/p>\n\n\n\n


\u9093\u81ea\u5c27 r3kapig\u6218\u961f\u961f\u5458<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6f14\u8bb2\u4e3b\u9898<\/strong> \uff1a<\/strong> CTF\u4e2d\u7b97\u6cd5Reverse\u548cCrypto\u7684\u4e00\u4e9b\u6742\u8c08
\u6f14\u8bb2\u5609\u5bbe\u4ecb\u7ecd\uff1a<\/strong>
ID:Oroggs,\u9ad8\u4e2d\u6bd5\u4e1a,\u76ee\u524d\u5f85\u4e1a,Eur3ka&&r3kapig\u6700\u83dc\u961f\u5458,\u559c\u6b22\u819cPizza,\u7ecf\u5e38\u5728\u591a\u4e2a\u7ebf\u4e0b\u6bd4\u8d5b\u4e2d\u5212\u6c34\u3002
\u5185\u5bb9\u6458\u8981\uff1a<\/strong>
\u6d45\u8c08\u4e00\u4e9b\u6280\u5de7\u5728\u89e3\u51b3\u7b97\u6cd5Reverse\u548cCrypto\u7c7bCTF\u9898\u76ee\u4e2d\u7684\u5b9e\u7528\u6027!\u66b4\u529b\u51fa\u5947\u8ff9!<\/p>\n\n\n\n


\u7eaa\u5b88\u9886 \u6d59\u6c5f\u5927\u5b66\u6559\u6388<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6f14\u8bb2\u4e3b\u9898<\/strong> \uff1a<\/strong> AI’s Security and AI2Security
\u6f14\u8bb2\u5609\u5bbe\u4ecb\u7ecd\uff1a<\/strong>
\u7eaa\u5b88\u9886\u83b7\u4f50\u6cbb\u4e9a\u7406\u5de5\u5b66\u9662\u7535\u5b50\u4e0e\u8ba1\u7b97\u673a\u5de5\u7a0b\u535a\u58eb\u5b66\u4f4d\u3001\u4f50\u6cbb\u4e9a\u5dde\u7acb\u5927\u5b66\u8ba1\u7b97\u673a\u79d1\u5b66\u535a\u58eb\u5b66\u4f4d\uff0c\u73b0\u4efb\u6d59\u6c5f\u5927\u5b66\u201c\u767e\u4eba\u8ba1\u5212\u201d\u7814\u7a76\u5458\u3001\u535a\u58eb\u751f\u5bfc\u5e08\uff0c\u517c\u4efb\u4f50\u6cbb\u4e9a\u7406\u5de5\u5b66\u9662Research Faculty\uff0c\u5165\u9009\u56fd\u5bb6\u201c\u9752\u5e74\u5343\u4eba\u201d\u3001\u6d59\u6c5f\u7701\u201c\u5343\u4eba\u8ba1\u5212\u201d\u3001\u6d59\u6c5f\u7701\u7279\u8058\u4e13\u5bb6\u3002\u4e3b\u8981\u7814\u7a76\u65b9\u5411\u4e3a\u6570\u636e\u9a71\u52a8\u5b89\u5168\u3001\u4eba\u5de5\u667a\u80fd\u5b89\u5168\u4e0e\u5927\u6570\u636e\u5206\u6790\uff0c\u53d1\u8868\u8bba\u6587100\u4f59\u7bc7\u3002\u66fe\u83b7\u4e2d\u56fd\u56fd\u5bb6\u4f18\u79c0\u7559\u5b66\u751f\u5956\u3001\u4e94\u9879\u6700\u4f73\u8bba\u6587\u5956\u3001GSU\u6770\u51fa\u7814\u7a76\u5956\u7b49\u3002
\u5185\u5bb9\u6458\u8981\uff1a<\/strong>
\u4eba\u5de5\u667a\u80fd\u88ab\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5404\u7c7b\u8ba1\u7b97\u673a\u7cfb\u7edf\uff0c\u5305\u62ec\u5b89\u5168\u548c\u9690\u79c1\u4fdd\u62a4\u5e94\u7528\u3002\u4e0e\u6b64\u540c\u65f6\uff0c\u4eba\u5de5\u667a\u80fd\u81ea\u8eab\u5b89\u5168\u6027\u4e5f\u906d\u53d7\u5bf9\u6297\u6837\u4f8b\u3001\u540e\u95e8\u653b\u51fb\u7b49\u5a01\u80c1\u3002\u672c\u6b21\u62a5\u544a\u5c06\u6839\u636e\u6211\u4eec\u8fd1\u671f\u7814\u7a76\u5de5\u4f5c\u7b80\u8981\u4ecb\u7ecd\uff1a\uff081\uff09\u4eba\u5de5\u667a\u80fd\u81ea\u8eab\u7684\u5b89\u5168\u95ee\u9898\uff0c\u5305\u62ec\u9762\u5411NLP\u7cfb\u7edf\u7684\u5bf9\u6297\u6837\u4f8b\u751f\u6210\u3001\u6a21\u578b\u5c42\u5bf9\u6297\u5b66\u4e60\u3001\u6df1\u5ea6\u5b66\u4e60\u5b89\u5168\u6027\u8bc4\u4f30\u7b49\uff1b\uff082\uff09\u4eba\u5de5\u667a\u80fd\u5e94\u7528\u4e8e\u5b89\u5168\u95ee\u9898\uff0c\u5305\u62ec\u6b3a\u8bc8\u68c0\u6d4b\u3001\u6a21\u7cca\u6d4b\u8bd5\u7b49\u3002<\/p>\n\n\n\n

\u5f20\u6bb7\u4e7e \u4fc4\u4ea5\u4fc4\u5dde\u7acb\u5927\u5b66\u6559\u6388<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6f14\u8bb2\u4e3b\u9898<\/strong> \uff1a<\/strong> A New Era of Computer Security
\u6f14\u8bb2\u5609\u5bbe\u4ecb\u7ecd\uff1a<\/strong>
Dr. Yinqian Zhang is an Associate Professor of the Department of Computer Science and Engineering at The Ohio State University. His research interest lies in computer security in general. His most prominent research is on the topic of side-channel security, particularly in the context of cloud computing, mobile computing, and confidential computing. Over the past ten years, he has published numerous high-quality peer-reviewed research papers in well-regarded conference proceedings and journals. As an expert in system security, he has been frequently invited to serve on the technical program committees of top security venues, such as IEEE S&P, ACM CCS, Usenix Security, and NDSS. Dr. Zhang was a recipient of the Google Ph.D. Fellowship in Security in 2013, a CAREER Award from the National Science Foundation in 2018, a Rising Star Award from the Association of Chinese Scholars in Computing in 2019, a Lumley Research Award and a Faculty Teaching Award from the Ohio State University in 2019. Dr. Zhang has been recognized as one of the AMiner Most Influential Scholars in security for his research contribution in the past ten years.<\/p>\n\n\n\n


\u90b9\u5fb7\u6e05 \u534e\u4e2d\u79d1\u6280\u5927\u5b66\u7f51\u7edc\u7a7a\u95f4\u5b89\u5168\u5b66\u9662\u526f\u9662\u957f\u3001\u6559\u6388<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6f14\u8bb2\u4e3b\u9898<\/strong> \uff1a<\/strong> \u4ece\u6e90\u4ee3\u7801\u6f0f\u6d1e\u6316\u6398\u8c08\u6709\u4ef7\u503c\u7814\u7a76
\u6f14\u8bb2\u5609\u5bbe\u4ecb\u7ecd\uff1a<\/strong>
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\u4e3b\u6301\u79d1\u6280\u90e8\u7f51\u7edc\u7a7a\u95f4\u5b89\u5168\u91cd\u70b9\u4e13\u9879\u8bfe\u9898\u3001863\u8bfe\u9898\u3001\u56fd\u5bb6\u81ea\u7136\u79d1\u5b66\u57fa\u91d1\u7b49\u591a\u4e2a\u56fd\u5bb6\u91cd\u8981\u9879\u76ee\uff0c\u4e5f\u662f973\u8ba1\u5212\u9879\u76ee\u201c\u4e91\u8ba1\u7b97\u5b89\u5168\u57fa\u7840\u7406\u8bba\u4e0e\u65b9\u6cd5\u7814\u7a76\u201d\u7684\u6280\u672f\u8d1f\u8d23\u4eba\uff0c\u51fa\u7248\u8457\u4f5c4\u90e8\uff0c\u53d1\u8868\u91cd\u8981\u5b66\u672f\u8bba\u6587100\u4f59\u7bc7\u3002\u7533\u8bf7\uff0f\u83b7\u5f9740\u4f59\u9879\u56fd\u5bb6\u6280\u672f\u53d1\u660e\u4e13\u5229\uff08\u5305\u62ec\uff1a3\u9879\u56fd\u9645\u4e13\u5229\uff09\u3002\u83b71\u9879\u6559\u80b2\u90e8\u6280\u672f\u53d1\u660e\u4e00\u7b49\u5956\uff0c1\u9879\u6e56\u5317\u7701\u79d1\u6280\u8fdb\u6b65\u4e00\u7b49\u5956\u3002<\/p>\n\n\n\n


\u5f20\u9633 \u5fb7\u56fdCISPA\u7814\u7a76\u4e2d\u5fc3\u7814\u7a76\u5458<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6f14\u8bb2\u4e3b\u9898\uff1aPrivacy Risks of Machine Learning Models \uff08\u673a\u5668\u5b66\u4e60\u6a21\u578b\u7684\u9690\u79c1\u98ce\u9669\uff09
\u6f14\u8bb2\u5609\u5bbe\u4ecb\u7ecd\uff1a<\/strong>
Yang Zhang (https:\/\/yangzhangalmo.github.io\/) is a research group leader at CISPA Helmholtz Center for Information Security, Saarbruecken, Germany. Previously, he was a postdoc working in the group of Michael Backes at CISPA from January 2017 to December 2018. Yang obtained his Ph.D. degree from University of Luxembourg in November 2016 under the supervision of Sjouke Mauw and Jun Pang. Prior to that, he obtained his bachelor (2009) and master (2012) degrees from Shandong University. Yang\u2019s research mainly concentrates on data privacy. Topics include machine learning privacy, biomedical privacy, and social network privacy. Besides, he also works on algorithmic fairness, authentication, urban computing, social media analysis, and data mining. Yang has published multiple papers at top venues in computer science including WWW, CCS, NDSS, and IJCAI. His work has recently received NDSS 2019 distinguished paper award.
\u5185\u5bb9\u6458\u8981\uff1a<\/strong>
The advancement of machine learning techniques has totally changed people’s way of living during the past decade. While keeping on improving our life quality, machine learning models may raise severe concerns about people\u2019s privacy. In this lecture, I will cover our recent work on assessing privacy risks stemming from machine learning models. First, I will talk about membership inference attacks against machine learning models. We show how to relax adversary\u2019s assumptions to achieve a model and data independent membership inference attack. Then, I will discuss our work on attacking online learning systems. We propose four effective attacks in this surface ranging from predicting attributes of the dataset used to update a machine learning model to reconstructing the updating dataset itself. In the end, I will discuss our lab’s ongoing works in machine learning security and privacy.<\/p>\n\n\n\n


\u6b66\u6210\u5c97 \u4e2d\u79d1\u9662\u8ba1\u7b97\u6240\u7814\u7a76\u5458\u7ea7\u9ad8\u5de5\uff0c\u535a\u58eb\u751f\u5bfc\u5e08<\/strong> <\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6f14\u8bb2\u4e3b\u9898<\/strong> \uff1a<\/strong> SafeHidden: \u901a\u8fc7\u6301\u7eed\u968f\u673a\u5316\u6765\u786e\u4fdd\u4fe1\u606f\u9690\u85cf\u7684\u9ad8\u6548\u6027\u548c\u5b89\u5168\u6027
\u6f14\u8bb2\u5609\u5bbe\u4ecb\u7ecd\uff1a<\/strong>\u6b66\u6210\u5c97\uff0c\u4e2d\u56fd\u79d1\u5b66\u9662\u8ba1\u7b97\u6280\u672f\u7814\u7a76\u6240\u7814\u7a76\u5458\u7ea7\u9ad8\u5de5\uff0c\u535a\u58eb\u751f\u5bfc\u5e08\u30022001\u5e74\u4e8e\u4e2d\u56fd\u79d1\u5b66\u9662\u8ba1\u7b97\u6280\u672f\u7814\u7a76\u6240\u83b7\u535a\u58eb\u5b66\u4f4d\u3002\u957f\u671f\u4ece\u4e8b\u57fa\u4e8e\u7f16\u8bd1\u6280\u672f\u7684\u8f6f\u4ef6\u5b89\u5168\u6280\u672f\u7684\u7814\u7a76\uff0c\u65e8\u5728\u901a\u8fc7\u7a0b\u5e8f\u5206\u6790\u6280\u672f\uff0c\u589e\u5f3a\u8f6f\u4ef6\u7684\u5b89\u5168\u6027\u3001\u53ef\u9760\u6027\u548c\u9ad8\u6548\u6027\u3002\u5728\u7a0b\u5e8f\u7f3a\u9677\u5b9a\u4f4d\u3001\u4e8c\u8fdb\u5236\u4ee3\u7801\u7684\u5206\u6790\u53d8\u6362\u53ca\u4f18\u5316\u3001\u8fd0\u884c\u65f6\u7a0b\u5e8f\u884c\u4e3a\u76d1\u6d4b\u3001\u5b89\u5168\u9632\u5fa1\u7b49\u65b9\u9762\uff0c\u6709\u7740\u8f83\u6df1\u7684\u6280\u672f\u79ef\u7d2f\u3002\u53d1\u8868\u8bba\u658730\u4f59\u7bc7\uff0c\u90e8\u5206\u53d1\u8868\u5728TSE\u3001ICSE\u3001ASE\u3001TPDS\u3001TACO\u3001SIGMETRICS\u3001PACT\u3001CGO\u3001VEE\u3001DATE\u7b49\u5b66\u672f\u4f1a\u8bae\u548c\u671f\u520a\u4e0a\uff0c\u83b7\u5f97\u6388\u6743\u4e13\u522916\u9879\uff0c\u8f6f\u4ef6\u8457\u4f5c\u67435\u9879\uff0c\u5e76\u4e8e2012\u5e74\u8363\u83b7\u5317\u4eac\u5e02\u79d1\u5b66\u6280\u672f\u4e8c\u7b49\u5956\u3002\u62c5\u4efb\u56fd\u9645\u5b66\u672f\u4f1a\u8baeCGO 2013\u5927\u4f1a\u4e3b\u5e2d\u3001APPT 2013\u7a0b\u5e8f\u59d4\u5458\u4f1a\u4e3b\u5e2d\uff0c\u8fd8\u62c5\u4efbCGO2015-2018\u3001PPoPP2017-2018\u3001PLDI2012\u3001CCGrid 2015\u3001ICPADS 2014\u3001PPPJ2014\u7b49\u4f1a\u8bae\u7684\u7a0b\u5e8f\u59d4\u5458\u4f1a\u59d4\u5458\u3002
\u5185\u5bb9\u6458\u8981\uff1a<\/strong>
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\"\"<\/figure>\n\n\n\n

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