Ph.D Symposium of the European Conference on Service-Oriented and Cloud Computing (ESOCC) 2013, September 2013.
The advent of cloud computing has provided the opportunity to externalize the identity management processes, shaping what has been called Identity Management as a Service (IDaaS). However, as in the case of other cloud-based services, IDaaS brings with it great concerns regarding security and privacy, such as the loss of control over the outsourced data. As part of this PhD thesis, we analyze these concerns and propose BlindIdM, a model for privacy-preserving IDaaS with a focus on data privacy protection through the use of proxy re-encryption.
23rd European Symposium on Research in Computer Security (ESORICS 2018), LNCS 11099, Springer, pp. 373-392, 2018. DOI
Location privacy has mostly focused on scenarios where users remain static. However, investigating scenarios where the victims present a particular mobility pattern is more realistic. In this paper, we consider abstract attacks on services that provide location information on other users in the proximity. In that setting, we quantify the required effort of the attacker to localize a particular mobile victim. We prove upper and lower bounds for the effort of an optimal attacker. We experimentally show that a Linear Jump Strategy (LJS) practically achieves the upper bounds for almost uniform initial distributions of victims. To improve performance for less uniform distributions known to the attacker, we propose a Greedy Updating Attack Strategy (GUAS). Finally, we derive a realistic mobility model from a real-world dataset and discuss the performance of our strategies in that setting.