Issue 2 Editors’ Introduction Kostas Chatzikokolakis (University of Athens), Aaron Johnson (U.S. Naval Research Laboratory) Illuminating the Dark or how to recover what should not be seen in FE-based …

Abstract: Classification algorithms/tools become more and more powerful and pervasive. Yet, for some use cases, it is necessary to be able to protect data privacy while benefiting from the functionalities …

Scaling Up Anonymous Communication with Efficient Nanopayment Channels Abstract: Tor, the most widely used and well-studied traffic anonymization network in the world, suffers from limitations in its …

1.1 Our Contributions The core of our paper consists of two novel contribu-tions: (i) a secure two-party protocol for sparse inner products, and (ii) a mechanism for extracting differ-entially private IDF …

Abstract: We report on a mixed-method, comparative study investigating whether there is a difference between privacy concerns expressed about pet wearables as opposed to human wearables – and …

Abstract: Tor, the most widely used and well-studied traffic anonymization network in the world, suffers from limitations in its network diversity and performance. We propose to mitigate both problems …

Benjamin VanderSloot*, Sergey Frolov, Jack Wampler, Sze Chuen Tan, Irv Simpson, Michalis Kallitsis, J. Alex Halderman, Nikita Borisov, and Eric Wustrow

Abstract: Differential privacy provides strong privacy guarantees simultaneously enabling useful insights from sensitive datasets. However, it provides the same level of protection for all elements (individuals …

Abstract: Differential privacy is a mathematical framework for privacy-preserving data analysis. Changing the hyperparameters of a differentially private algorithm allows one to trade off privacy and …

Abstract: Shortly after it was first introduced in 2006, differential privacy became the flagship data privacy definition. Since then, numerous variants and extensions were proposed to adapt it to …