INTACT: Compact Storage of Data Streams in Mobile Devices to Unlock User Privacy at the Edge

TL;DR

This paper introduces INTACT, a framework that uses compact data stream storage and privacy-preserving techniques on mobile devices to enhance user privacy at the edge in ubiquitous computing environments.

Contribution

It presents the FLI technique for efficient data stream compression and the Divide & Stay method for privacy-preserving POI inference, implemented on Android and iOS.

Findings

FLI achieves significant data compression on mobile devices.

Divide & Stay effectively preserves user privacy during POI inference.

INTACT demonstrates practical deployment on mainstream mobile platforms.

Abstract

Data streams produced by mobile devices, such as smartphones, offer highly valuable sources of information to build ubiquitous services. Such data streams are generally uploaded and centralized to be processed by third parties, potentially exposing sensitive personal information. In this context, existing protection mechanisms, such as Location Privacy Protection Mechanisms (LPPMs), have been investigated. Alas, none of them have actually been implemented, nor deployed in real-life, in mobile devices to enforce user privacy at the edge. Moreover, the diversity of embedded sensors and the resulting data deluge makes it impractical to provision such services directly on mobiles, due to their constrained storage capacity, communication bandwidth and processing power. This article reports on the FLI technique, which leverages a piece-wise linear approximation technique to capture compact representations of data streams in mobile devices. Beyond the FLI storage layer, we introduce Divide \& Stay, a new privacy preservation technique to execute Points of Interest (POIs) inference. Finally, we deploy both of them on Android and iOS as the INTACT framework, making a concrete step towards enforcing privacy and trust in ubiquitous computing systems.