Achieving Guaranteed Anonymity in GPS Traces via Uncertainty-Aware Path Cloaking

  1.ABSTRACT :

                          The integration of Global Positioning System (GPS) receivers and sensors into mobile devices has enabled collaborative sensing applications, which monitor the dynamics of environments through opportunistic collection of data from many users’ devices. One example that motivates this project is a probe-vehicle-based automotive traffic monitoring system, which estimates traffic congestion from GPS velocity measurements reported from many drivers. This work considers the problem of achieving guaranteed anonymity in a locational data set that includes location traces from many users, while maintaining high data accuracy. We consider two methods to

re-identify anonymous location traces, target tracking, and home identification, and observe that known privacy algorithms cannot achieve high application accuracy requirements or fail to provide privacy guarantees for drivers in low-density areas. To overcome these challenges, we derive a novel time-to-confusion criterion to characterize privacy in a locational data set and propose a disclosure control algorithm (called uncertainty-aware path cloaking algorithm) that selectively reveals GPS samples to limit the maximum time-to confusion

for all vehicles. Through trace-driven simulations using real GPS traces from 312 vehicles, we demonstrate that this algorithm effectively limits tracking risks, in particular, by eliminating tracking outliers. It also achieves significant data accuracy improvements compared to known algorithms. We then present two enhancements to the algorithm. First, it also addresses the home identification risk by reducing location information revealed at the start and end of trips. Second, it also considers heading information reported by users in the tracking model. This version can thus protect users who are moving in dense areas but in a different direction from the majority

  2. EXISTING SYSTEM :

  

                     Several techniques have been existing to protect against location privacy breaches through inference methods. However, we are  aware of only one class of techniques, spatial cloaking algorithms for   k-anonymity, which can guarantee a defined degree of anonymity for all users. Other algorithms can be categorized as best-effort algorithms that increase average privacy levels, but offer no specific guaranteed privacy level for an individual user.

  2.1 Spatial Cloaking for Guaranteed Privacy

                                          k-anonymity  formalizes the notion of strong anonymity and complementary algorithms exist to anonymize database tables. The key idea underlying these algorithms is to generalize a data record until it is indistinguishable from the records of at least k # 1 other individuals. Specifically, for location information, spatial cloaking algorithms have been proposed  that reduce the spatial accuracy of each location sample until it meets the k-anonymity constraint. To achieve this, the algorithms require knowledge of the nearby vehicles’ positions, thus, they are usually implemented on a trusted server with access to all vehicles’ current position.

  2.2 Best-Effort Algorithms for Probabilistic Privacy

                                                      Best-effort algorithms suppress information only in certain high-density areas rather than uniformly over the traces as the sub sampling approach. The motivation for these algorithms is that path suppression in high-density areas increases the chance for confusing or mixing several different traces. The path confusion  algorithm also concentrates on such high-density areas although it perturbs location samples rather than suppressing them. These techniques increase the chance of confusion in high density areas, but they also cannot guarantee strong privacy in low-density areas where paths only infrequently meet. Thus, in terms of worst-case privacy guarantees, their advantage over sub sampling remains unclear.

  2.3 Privacy of Best Effort sub sampling

                              Best-effort privacy techniques do not fully protect against home identification. While the evaluated home identification intrusion technique

suffered from many false positives, this mechanism is at least effective as an automated pre filtering step that can be followed by manual inspection.  

   3. PROPOSED SYSTEM

                                      We propose the time-to-confusion metric and cloaking algorithms to address privacy in an anonymous set of time-series location traces. We considered two specific privacy risks in anonymous location traces

target tracking and place identification and found that these allow tracking and re-identifying data subjects in anonymous traces, particularly in areas with low user density. We quantify the tracking risk through the time-to-confusion metric and develop the uncertainty-aware path cloaking algorithm, which can filter a set of anonymous GPS traces to guarantee a maximum privacy-risk level (specified as time-to-confusion). Using a real-world GPS data set, we measure the privacy gain and the achieved data quality for the proposed solutions compared to a baseline random sampling technique. We show that our uncertainty-aware path cloaking effectively guarantees worst-case tracking bounds (i.e.,outliers), while achieving significant data accuracy improvements.

                                             Development of an uncertainty-aware path cloaking algorithm that can guarantee a specified maximum time-to-confusion and protect against home identification risks. Demonstration through experiments on real-world GPS traces that this algorithm limits maximum time-to-confusion while providing more accurate location data than a random sampling baseline algorithm. In particular, it offers guaranteed protection for users that move into low-density areas.

  4.HARDWARE REQUIREMENTS:

•         System                : Pentium IV 2.4 GHz.

•         Hard Disk            : 40 GB.

•         Floppy Drive       : 1.44 MB.

•         Monitor                : 15 VGA Colour.

•         Mouse                 : Logitech.

•         Ram                     : 256 MB.

   5.Software Requirements:

•         Operating System       : - Windows XP Professional.

•         Front End                               : - Asp .Net 2.0.

•         Coding Language       : - Visual C# .Net.