Liu, YinCruz, Breno DantasTilevich, Eli2023-01-232023-01-232022-01-01978-3-030-99202-61867-8211http://hdl.handle.net/10919/113353To personalize modern mobile services (e.g., advertisement, navigation, healthcare) for individual users, mobile apps continuously collect and analyze sensor data. By sharing their sensor data collections, app providers can improve the quality of mobile services. However, the data privacy of both app providers and users must be protected against data leakage attacks. To address this problem, we present differentially privatized on-device sharing of sensor data, a framework through which app providers can safely collaborate with each other to personalize their mobile services. As a trusted intermediary, the framework aggregates the sensor data contributed by individual apps, accepting statistical queries against the combined datasets. A novel adaptive privacy-preserving scheme: 1) balances utility and privacy by computing and adding the required amount of noise to the query results; 2) incentivizes app providers to keep contributing data; 3) secures all data processing by integrating a Trusted Execution Environment. Our evaluation demonstrates the framework’s efficiency, utility, and safety: all queries complete in <10 ms; the data sharing collaborations satisfy participants’ dissimilar privacy/utility requirements; mobile services are effectively personalized, while preserving the data privacy of both app providers and users.Pages 19-4123 page(s)application/pdfenIn CopyrightMobile privacyConference proceeding2023-01-20https://doi.org/10.1007/978-3-030-99203-3_2434Tilevich, Eli [0000-0003-2415-6926]1867-822X