PULP: Achieving Privacy and Utility Trade-Off in User Mobility Data

Abstract

Leveraging location information in location-based services leads to improving service utility through geocontextualization. However, this raises privacy concerns as new knowledge can be inferred from location records, such as user's home and work places, or personal habits. Although Location Privacy Protection Mechanisms (LPPMs) provide a means to tackle this problem, they often require manual configuration posing significant challenges to service providers and users. Moreover, their impact on data privacy and utility is seldom assessed. In this paper, we present PULP, a model-driven system which automatically provides user-specific privacy protection and contributes to service utility via choosing adequate LPPM and configuring it. At the heart of PULP is nonlinear models that can capture the complex dependency of data privacy and utility for each individual user under given LPPM considered, i.e., Geo-Indistinguishability and Promesse. According to users' preferences on privacy and utility, PULP efficiently recommends suitable LPPM and corresponding configuration. We evaluate the accuracy of PULP's models and its effectiveness to achieve the privacy-utility trade-off per user, using four real-world mobility traces of 770 users in total. Our extensive experimentation shows that PULP ensures the contribution to location service while adhering to privacy constraints for a great percentage of users, and is orders of magnitude faster than non-model based alternatives.

Automatic Privacy and Utility Preservation for Mobility Data: A Nonlinear Model-Based Approach

“…Moreover, the convergence is not ensured and consequently there is no guarantee that the objectives are actually met. PULP is first presented in [7]. In this paper, we extend this previous work by developing three more configuration laws enabling a dataset owner to specify several combinations of privacy and utility objectives, and we present extensive experimental evaluations for both the modeling and the configuration parts of PULP.…”

FOUGERE: User-Centric Location Privacy in Mobile Crowdsourcing Apps

“…Location privacy protection mechanisms (LPPMs) are particularly interesting to limit user privacy leaks [5]. A large body of the related work has been devoted towards the latest stages of mobile crowdsourcing campaigns by improving the privacy properties of datasets once uploaded to remote servers [23,28,35].…”

“…We observed that these two properties are related in practice, as the application of LPPMs tends to decrease the utility of the crowdsourced dataset. This observation calls for the identification of a privacy and utility trade-off in the context of mobile crowdsourcing systems, as acknowledged by [5].…”

MooD

“…For instance, ALP (Adaptive Location Privacy) [25] a framework which enables an automatic configuration of the LPPM parameters using simulated annealing. Also, PULP [9] is another system which automatically configures LPPMs according to users' objectives in term of privacy and utility. MooD is complementary to those configuration frameworks.…”