Sanitization of Call Detail Records via Differentially-private Summaries
Mohammad Alaggan, S\'ebastien Gambs, Stan Matwin, Eriko Souza, and, Mohammed Tuhin

TL;DR
This paper introduces a privacy-preserving method for analyzing human mobility using sanitized call detail records, employing differentially-private Bloom filters to enable secure, accurate mobility pattern summaries.
Contribution
The paper presents a novel, efficient approach using differentially-private Bloom filters to securely summarize mobility data from CDRs, improving privacy without sacrificing utility.
Findings
Method achieves high utility comparable to non-private solutions
Ensures strong differential privacy guarantees
Efficient in time and space for real-world datasets
Abstract
In this work, we initiate the study of human mobility from sanitized call detail records (CDRs). Such data can be extremely valuable to solve important societal issues such as the improvement of urban transportation or the understanding on the spread of diseases. One of the fundamental building block for such study is the computation of mobility patterns summarizing how individuals move during a given period from one area e.g., cellular tower or administrative district) to another. However, such knowledge cannot be published directly as it has been demonstrated that the access to this type of data enable the (re-)identification of individuals. To answer this issue and to foster the development of such applications in a privacy-preserving manner, we propose in this paper a novel approach in which CDRs are summarized under the form of a differentially-private Bloom filter for the purpose…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Privacy-Preserving Technologies in Data · Opportunistic and Delay-Tolerant Networks
