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Networks entail vulnerable and sensitive information that pose serious privacy threats. In this paper, we introduce, k-core attack, a new attack model which stems from the k-core decomposition principle. K-core attack undermines the privacy of some state-of-the-art techniques. We propose a novel structural anonymization technique called (k, Δ)-Core(More)
The embrace of pervasive devices accounts for the production of a massive amount of location data. While multitudes of algorithms have been used for location clustering, most of them focus on the proximity clustering of locations rather than on their location contexts. In this work, we propose a novel context-based location clustering technique that(More)
Location privacy and security of spatio-temporal data has come under high scrutiny in the past years. This has rekindled enormous research interest. So far, most of the research studies that attempt to address location privacy are based on the <i>k</i>-Anonymity privacy paradigm. In this paper, we propose a novel technique to ensure location privacy in(More)
The widespread adoption of ubiquitous devices does not only facilitate the connection of billions of people, but has also fuelled a culture of sharing rich, high resolution locations through check-ins. Despite the profusion of GPS and WiFi driven location prediction techniques, the sparse and random nature of check-in data generation have ushered diverse(More)
The prediction of future locations is of enormous research interest, partly due to the fast growing number of users of pervasive devices, as well as the tons of spatiotemporal data generated by such devices. In this paper, we propose a novel enhanced Next Location prediction technique which utilizes a trajectory model called Time Mobility Context(More)
Mobility data is Big Data. Modeling such raw big location data is quite challenging in terms of quality and runtime efficiency. Mobility data emanating from smart phones and other pervasive devices consists of a combination of spatio-temporal dimensions, as well as some additional contextual dimensions that may range from social network activities, diseases(More)
Advances in sensor and ubiquitous technologies have contributed to the broad scale adoption of pervasive devices. Context or activity recognition from sensor signals is an emerging area that has garnered huge research interest. In this paper, we propose a novel predictive model that utilizes dyadic wavelet transform, vector quantization and Hidden Markov(More)
The rush for personalized user information, triggered by the daily generation of a staggering amount of geospatial data from multitude platforms, is leading to an erosion of users' location privacy. To ensure the privacy of moving objects on road networks, most existing works do not enforce a strict constrain that the anonymized or perturbed geospatial(More)
Geospatial data emanating from GPS-enabled pervasive devices reflects the mobility and interactions between people and places, and poses serious threats to privacy. Most of the existing location privacy works are based on the k-Anonymity privacy paradigm. In this paper, we employ a different and stronger privacy definition called Differential Privacy. We(More)