Igor Bilogrevic

Anindya Maiti, Murtuza Jadliwala, Jibo He et al.

Smartwatches enable many novel applications and are fast gaining popularity. However, the presence of a diverse set of on-board sensors provides an additional attack surface to malicious software and services on these devices. In this paper, we investigate the feasibility of key press inference attacks on handheld numeric touchpads by using smartwatch motion sensors as a side-channel. We consider different typing scenarios, and propose multiple attack approaches to exploit the characteristics of the observed wrist movements for inferring individual key presses. Experimental evaluation using commercial off-the-shelf smartwatches and smartphones show that key press inference using smartwatch motion sensors is not only fairly accurate, but also comparable with similar attacks using smartphone motion sensors. Additionally, hand movements captured by a combination of both smartwatch and smartphone motion sensors yields better inference accuracy than either device considered individually.

Igor Bilogrevic, Julien Freudiger, Emiliano De Cristofaro et al.

Over the past few years, online service providers have started gathering increasing amounts of personal information to build user profiles and monetize them with advertisers and data brokers. Users have little control of what information is processed and are often left with an all-or-nothing decision between receiving free services or refusing to be profiled. This paper explores an alternative approach where users only disclose an aggregate model -- the "gist" -- of their data. We aim to preserve data utility and simultaneously provide user privacy. We show that this approach can be efficiently supported by letting users contribute encrypted and differentially-private data to an aggregator. The aggregator combines encrypted contributions and can only extract an aggregate model of the underlying data. We evaluate our framework on a dataset of 100,000 U.S. users obtained from the U.S. Census Bureau and show that (i) it provides accurate aggregates with as little as 100 users, (ii) it generates revenue for both users and data brokers, and (iii) its overhead is appreciably low.