Iraklis Leontiadis

Learn More
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)
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)
—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)
We address the problem of substring searchable encryption. A single user produces a big stream of data and later on wants to learn the positions in the string that some patterns occur. Although current techniques exploit auxiliary data structures to achieve efficient substring search on the server side, the cost at the user side may be prohibitive. We(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)
—Existing privacy controls based on access control techniques do not prevent massive dissemination of private data by unauthorized users. We suggest a usage control enforcement scheme that allows users to gain control over their data during its entire lifetime. The scheme is based on a peer-to-peer architecture whereby a different set of peers is randomly(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)
  • 1