(Apologies for crossposting.) CALL FOR PAPERS
ICML 2014 Workshop on Learning, Security and Privacy Beijing, China, 25 or 26 June, 2014 (TBD) https://sites.google.com/site/learnsecprivacy2014/ ---------------------------------------------------------------- Important Dates: - Submission deadline: 28 March, 2014 - Notification of acceptance: 18 April, 2014 ---------------------------------------------------------------- Workshop overview: Many machine learning settings give rise to security and privacy requirements which are not well-addressed by traditional learning methods. Security concerns arise in intrusion detection, malware analysis, biometric authentication, spam filtering, and other applications where data may be manipulated - either at the training stage or during the system deployment - to reduce prediction accuracy. Privacy issues are common to the analysis of personal and corporate data ubiquitous in modern Internet services. Learning methods addressing security and privacy issues face an interplay of game theory, cryptography, optimization and differential privacy. Despite encouraging progress in recent years, many theoretical and practical challenges remain. Several emerging research areas, including stream mining, mobility data mining, and social network analysis, require new methodical approaches to ensure privacy and security. There is also an urgent need for methods that can quantify and enforce privacy and security guarantees for specific applications. The ever increasing abundance of data raises technical challenges to attain scalability of learning methods in security and privacy critical settings. These challenges can only be addressed in the interdisciplinary context, by pooling expertise from the traditionally disjoint fields of machine learning, security and privacy. To encourage scientific dialogue and foster cross-fertilization among these three fields, the workshop invites original submissions, ranging from ongoing research to mature work, in any of the following core subjects: - Statistical approaches for privacy preservation. - Private decision making and mechanism design. - Metrics and evaluation methods for privacy and security. - Robust learning in adversarial environments. - Learning in unknown / partially observable stochastic games. - Distributed inference and decision making for security. - Application-specific privacy preserving machine learning and decision theory. - Secure multiparty computation and cryptographic approaches for machine learning. - Cryptographic applications of machine learning and decision theory. - Security applications: Intrusion detection and response, biometric authentication, fraud detection, spam filtering, captchas. - Security analysis of learning algorithms - The economics of learning, security and privacy. Submission instructions: Submissions should be in the ICML 2014 format, with a maximum of 6 pages (including references). Work must be original. Accepted papers will be made available online at the workshop website. Submissions need not be anonymous. Submissions should be made through EasyChair: https://www.easychair.org/conferences/?conf=lps2014. For detailed submission instructions, please refer to the workshop website. Organizing committee: Christos Dimitrakakis (Chalmers University of Technology, Sweden). Pavel Laskov (University of Tuebingen, Germany). Daniel Lowd (University of Oregon, USA). Benjamin Rubinstein (University of Melbourne, Australia). Elaine Shi (University of Maryland, College Park, USA). Program committee: Michael Brückner (Amazon, Germany) Battista Biggio (University of Cagliari, Italy) Alvaro Cardenas (University of Texas, Dallas, USA) Kamalika Chaudhuri (UCSD, USA) Alex Kantchelian (UC Berkeley, USA) Aikaterini Mitrokotsa (Chalmers University, Sweden) Blaine Nelson (University of Potsdam, Germany) Konrad Rieck (University of Goettingen, Germany) Nedim Srndic (University ofr Tuebingen) Aaron Roth (University of Pennsylvania, USA) Risto Vaarandi (NATO CCDCOE, Estonia) Shobha Venkataraman (AT&T Research, USA) Ting-Fang Yen (EMC, USA)
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