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  1. PoPETs Proceedings — Volume 2016 - petsymposium.org

    Volume 2016 Issue 1 Editor’s Introduction Apu Kapadia (Indiana University Bloomington) Fingerprinting Mobile Devices Using Personalized Configurations [PDF]

  2. PoPETs Proceedings — Black-Box Accumulation: Collecting …

    Black-Box Accumulation: Collecting Incentives in a Privacy-Preserving Way Authors: Tibor Jager (Ruhr-University Bochum), Andy Rupp (Karlsruhe Institute of Technology) Volume: 2016 …

  3. Proceedings on Privacy Enhancing Technologies ; 2016 (4):237–254 Christoph Bösch*, Benjamin Erb, Frank Kargl, Henning Kopp, and Stefan Pfattheicher

  4. Tales from the Dark Side: Privacy Dark Strategies and Privacy Dark …

    Volume: 2016 Issue: 4 Pages: 237–254 DOI: https://doi.org/10.1515/popets-2016-0038 Download PDF Abstract: Privacy strategies and privacy patterns are fundamental concepts of the privacy …

  5. SoK: Privacy on Mobile Devices – It’s Complicated

    Volume: 2016 Issue: 3 Pages: 96–116 DOI: Download PDF Abstract: Modern mobile devices place a wide variety of sensors and services within the personal space of their users. As a …

  6. PoPETs Proceedings — Riffle - petsymposium.org

    Volume: 2016 Issue: 2 Pages: 115–134 DOI: https://doi.org/10.1515/popets-2016-0008 Download PDF Abstract: Existing anonymity systems sacrifice anonymity for efficient communication or …

  7. Proceedings on Privacy Enhancing Technologies ; 2016 (4):219–236 John M. Schanck*, William Whyte, and Zhenfei Zhang

  8. PoPETs Proceedings — Efficient Verifiable Range and Closest Point ...

    Volume: 2016 Issue: 4 Pages: 373–388 DOI: https://doi.org/10.1515/popets-2016-0045 Download PDF Abstract: We present an efficient method for answering one-dimensional range and …

  9. Proceedings on Privacy Enhancing Technologies ; 2016 (4):403–417 Steven Hill*, Zhimin Zhou, Lawrence Saul, and Hovav Shacham

  10. PoPETs Proceedings — Students and Taxes: a Privacy-Preserving …

    Volume: 2016 Issue: 3 Pages: 117–135 DOI: Download PDF Abstract: We describe the use of secure multi-party computation for performing a large-scale privacypreserving statistical study …