Privacy-preserving activity scheduling on mobile devices

Abstract

Progress in mobile wireless technology has resulted in the increased use of mobile devices to store and manage users' personal schedules. Users also access popular context-based services, typically provided by third-party providers, by using these devices for social networking, dating and activitypartner searching applications. Very often, these applications need to determine common availabilities among a set of user schedules. The privacy of the scheduling operation is paramount to the success of such applications, as often users do not want to share their personal schedules with other users or third-parties. Previous research has resulted in solutions that provide privacy guarantees, but they are either too complex or do not fit well in the popular user-provider operational model. In this paper, we propose practical and privacy-preserving solutions to the server-based scheduling problem. Our novel algorithms take advantage of the homomorphic properties of well-known cryptosystems in order to privately compute common user availabilities. We also formally outline the privacy requirements in such scheduling applications and we implement our solutions on real mobile devices. The experimental measurements and analytical results show that the proposed solutions not only satisfy the privacy properties but also fare better, in regard to computation and communication efficiency, compared to other well-known solutions.

Publicly Verifiable Private Aggregation of Time-Series Data

“…Besides the literature mentioned above, there are other related schemes on privacy preserving data aggregation in various application settings: Ács et al [1], Chan et al [8] and Kursawe et al [12] provide schemes for usage in the context of smart metering. Furthermore, De Cristofaro and Soriente [9] and Shi et al [16] give a constructions for participatory sensing applications and Bilogrevic et al [4] and Bilogrevic et al [3] offer appointment scheduling schemes, but none of them can deal with verifiability. Moreover, in [9] and [16] the decryption is done separately from the data aggregation (just as in [4] and [3]) and there are intermediary actors between the data-providers and the data aggregator, which is often rather impractical.…”

“…Furthermore, De Cristofaro and Soriente [9] and Shi et al [16] give a constructions for participatory sensing applications and Bilogrevic et al [4] and Bilogrevic et al [3] offer appointment scheduling schemes, but none of them can deal with verifiability. Moreover, in [9] and [16] the decryption is done separately from the data aggregation (just as in [4] and [3]) and there are intermediary actors between the data-providers and the data aggregator, which is often rather impractical.…”