Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases

By Osman Abul, Francesco Bonchi, Mirco Nanni. IEEE International Conference on Data Engineering, 2008

558 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/f7eca95f5c2c96618259810593e35103ad0cd59d

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Citations in the corpus

Unveiling the complexity of human mobility by querying and mining massive trajectory data (2011, background): “Other important facets of M-Atlas have not been discussed in this paper, including (i) the privacy-preserving data publishing and mining techniques designed to transform trajectory data sets into anonymous forms in such a way that strong privacy-protection guarantees can coexist with high data utili”
Towards trajectory anonymization: a generalization-based approach (2008, background): “We are also aware of very recent, independent research [8, 27, 43] addressing the problem of preserving privacy in static trajectory databases.”
Trajectory Data Mining (2015, background): “Instead of making a trajectory more certain, a series of techniques aim to protect a user from the privacy leak caused by the disclosure of the user’s trajectories [1][111][25].”
Differentially private sequential data publication via variable-length n-grams (2012, background): “[1] propose the (k, δ)-anonymity model based on the inherent imprecision of sampling and positioning systems, where δ represents the possible location imprecision.”
Privacy-preserving data publishing: A survey of recent developments (2010, background): “Typical examples are the deployments of mobile devices with location-based services [Abul et al. 2008; Atzori et al. 2007; Hengartner 2007; You et al. 2007], sensor networks, and social networks.”
Privacy Preserving Image Queries for Camera Localization (2019, background): “Other anonymization approaches for location privacy for mobile systems have been explored [5, 6, 8, 21, 42].”
Computing with Spatial Trajectories (2011, methodology): “[1], who simply use the sum of distances between the corresponding pairs of points to measure the similarity.”