Jairo Giraldo (University of Utah), Alvaro Cardenas (UC Santa Cruz), Murat Kantarcioglu (UT Dallas), Jonathan Katz (George Mason University)

Differential Privacy has emerged in the last decade as a powerful tool to protect sensitive information. Similarly, the last decade has seen a growing interest in adversarial classification, where an attacker knows a classifier is trying to detect anomalies and the adversary attempts to design examples meant to mislead this classification.

Differential privacy and adversarial classification have been studied separately in the past. In this paper, we study the problem of how a strategic attacker can leverage differential privacy to inject false data in a system, and then we propose countermeasures against these novel attacks. We show the impact of our attacks and defenses in a real-world traffic estimation system and in a smart metering system.

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Genotype Extraction and False Relative Attacks: Security Risks to...

Peter Ney (University of Washington), Luis Ceze (University of Washington), Tadayoshi Kohno (University of Washington)

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Unicorn: Runtime Provenance-Based Detector for Advanced Persistent Threats

Xueyuan Han (Harvard University), Thomas Pasquier (University of Bristol), Adam Bates (University of Illinois at Urbana-Champaign), James Mickens (Harvard University), Margo Seltzer (University of British Columbia)

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Heterogeneous Private Information Retrieval

Hamid Mozaffari (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst)

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MassBrowser: Unblocking the Censored Web for the Masses, by...

Milad Nasr (University of Massachusetts Amherst), Hadi Zolfaghari (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst), Amirhossein Ghafari (University of Massachusetts Amherst)

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