Assessing the trustworthiness of location data corresponding to individuals is essential in several applications, such as forensic science and epidemic control. To obtain accurate and trustworthy location data, analysts must often gather and correlate information from several independent sources, e.g., physical observation, witness testimony, surveillance footage, etc. However, such information may be fraudulent, its accuracy may be low, and its vol-
ume may be insufficient to ensure highly trustworthy data. On the other hand, recent advancements in mobile computing and positioning systems, e.g., GPS-enabled cell phones, highway sensors, etc., bring new and effective technological means to track the location of an individual. Nevertheless, collection and sharing of such data must be done in ways that do not violate an individual’s right to personal privacy. Previous research efforts acknowledged the importance of assessing location data trustworthiness, but they assume that data is available to the analyst in direct, unperturbed form. However, such an assumption is not realistic, due to the fact that repositories of personal location data must conform to privacy regulations.
In this paper, we study the challenging problem of refining trustworthiness of location data with the help of large repositories of anonymized information. We show how two important trustworthiness evaluation techniques, namely common pattern analysis and conflict/support analysis, can benefit from the use of anonymized location data. We have implemented a prototype of the proposed privacy-preserving trustworthiness evaluation techniques, and the
experimental results demonstrate that using anonymized data can significantly help in improving the accuracy of location trustworthiness assessment.
security, data trustworthiness, loction data, privacy
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