Iraklis Leontiadis

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With the advent of networking applications collecting user data on a massive scale, the privacy of individual users appears to be a major concern. The main challenge is the design of a solution that allows the data analyzer to compute global statistics over the set of individual inputs that are protected by some confidentiality mechanism. Joye et al. [7](More)
Enhancing anonymity in the Session Initiation Protocol (SIP) is much more than sealing participants' identities. It requires methods to unlink the communication parties and relax their proximity identification. These requirements should be fulfilled under several prerequisites, such as time limitation for session establishment, involvement of several(More)
The study of real-world communication systems via complex network models has greatly expanded our understanding on how information flows, even in completely decentralized architectures such as mobile wireless networks. Nonetheless, static network models cannot capture the time-varying aspects and, therefore, various temporal metrics have been introduced. In(More)
Current applications tend to use personal sensitive information to achieve better quality with respect to their services. Since the third parties are not trusted the data must be protected such that individual data privacy is not compromised but at the same time operations on it would be compatible. A wide range of data analysis operations entails a(More)
Existing work on data collection and analysis for aggregation is mainly focused on confidentiality issues. That is, the untrusted Aggregator learns only the aggregation result without divulging individual data inputs. In this paper we extend the existing models with stronger security requirements. Apart from the privacy requirements with respect to the(More)
Smart meters are widely deployed to provide fine-grained information pertaining to tenant power consumption. These data are analyzed by suppliers for more accurate statistics, energy consumption predictions and personalized billing. Indirectly this aggregation of data can reveal personal information of tenants such as number of persons in a house, vacation(More)
The progress in communication and hardware technology increases the computational capabilities of personal devices. Data is produced massively from ubiquitous devices that cannot be stored locally. Moreover, third party authorities in order to increase their value in the market with more knowledge, seek to collect individual data inputs, such that they can(More)