Abstract
Up to now, a large amount of trajectory data have been collected by trusted servers because of the wide use of location-based services. One can extract useful information via an analysis of trajectory data. However, the privacy of trajectory bodies risks being inadvertently divulged to others. Therefore, the trajectory data should be properly processed for privacy protection before being released to unknown analysts. This paper proposes a privacy protection scheme for publishing the trajectories with personalized privacy requirements based on the translocation of trajectory points. The algorithm not only enables the published trajectory points to meet the personalized privacy requirements regarding desensitization and anonymity but also preserves the positions of all trajectory points. Our algorithm trades the loss in mobility patterns for the advantage in the similarity of trajectory distance. Related experiments on trajectory data sets with personalized privacy requirements have verified the effectiveness and the efficiency of our algorithm.
Highlights
With the maturity of location technology and wireless communication technology, location-based service (LBS) becomes more and more popular
To the TDs with personalized privacy requirements, the data distortion generated by the personalized algorithm is of the same magnitude as that generated by the nonpersonalized algorithm (NWA versus CPPP and IPKN versus Translocation-based Personalized Privacy Protection (TPPP)), while these two kinds of algorithms have the same granularity
We propose a Translocation-based Personalized Privacy Protection (TPPP) method for publishing trajectories
Summary
With the maturity of location technology and wireless communication technology, location-based service (LBS) becomes more and more popular. E analysis of trajectory data can reveal useful knowledge, but it threatens the privacy of the trajectory bodies. E location service requests (LSR) contain personalized privacy requirements of the body on that location, and the requirements can inform the SA of body’s sensitivity. For the trajectory data with personalized privacy requirements, the existing privacy protection methods usually resist the identity link attack of trajectories and realize the personalized anonymity of trajectories [1,2,3]. We propose a Translocation-based Personalized Privacy Protection (TPPP) for publishing the trajectories with personalized privacy requirements on trajectory points. We assume that the personalized privacy requirements set by the body of the trajectory include sensitivity and anonymous threshold of the trajectory points. Is method realizes the personalized privacy protection through the translocations of the trajectory points. Each trajectory point pi has specific sensitivity thresholds di, and the translocations of trajectory points cannot be assigned into the sensitive area in red. e trajectory publisher defines a maximum of distortion distance and is represented as dmax. e translocations of trajectory points cannot exceed the range of the dotted line in green. e anonymous threshold ki of pi is not marked in Figure 1. ki means the probability that inferring
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