Research On Privacy-preserving Algorithm For Accompanied Travels Based On Local Differential Privacy

Over the last few years,the widespread usage of smartphones has enabled ubiquitous application of Location-Based Services(LBS).Meanwhile,with the presence of unlawful exploitation of user’s personal data by untrusted location service providers,the publication of unprotected original location data streams to those LBS could lead to serious leakage of users’ social relations privacy: it’s easy to tell if two users have social ties by analyzing trajectory data published when they are traveling together.In order to tackle the aforementioned privacy leakage problem,firstly,a single location publication algorithm is designed and customized to users’ accompanied traveling privacy protection setting while satisfying the definition of Local Differential Privacy(LDP).Secondly,a privacy concept for data streams that is adaptable to ranging data publishing frequency,along with a privacy-budget-allocation algorithm satisfying this concept,is proposed to tackle privacy threats in continuous location publication settings.Finally,the above two algorithms are combined to design Privacy-preserving Algorithm For Accompanied Travels(PAFAT)based on LDP,which is the final solution to protecting users’ accompanied travels privacy.In order to measure the usefulness of PAFAT,relative position vector rotation angle(or simply rotation angle)is proposed to gauge the entanglement of two trajectories.Experiments conducted on multiple trajectory pairs selected from a real traveling dataset shows that,in terms of privacy metrics such as rotation angle and utility metrics such as POI utility,PAFAT performs better than two state-of-the-art algorithms,which demonstrates the effectiveness of PAFAT under accompanied traveling setting.