A Survey on Homomorphic Encryption Schemes: Theory and Implementation

Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns. Especially with popular cloud services, the control over the privacy of the sensitive data is lost. Even when the keys are not shared, the encrypted material is shared with a third party that does not necessarily need to access the content. Moreover, untrusted servers, providers, and cloud operators can keep identifying elements of users long after users end the relationship with the services. Indeed, Homomorphic Encryption (HE), a special kind of encryption scheme, can address these concerns as it allows any third party to operate on the encrypted data without decrypting it in advance. Although this extremely useful feature of the HE scheme has been known for over 30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE) scheme, which allows any computable function to perform on the encrypted data, was introduced by Craig Gentry in 2009. Even though this was a major achievement, different implementations so far demonstrated that FHE still needs to be improved significantly to be practical on every platform. First, we present the basics of HE and the details of the well-known Partially Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which are important pillars of achieving FHE. Then, the main FHE families, which have become the base for the other follow-up FHE schemes are presented. Furthermore, the implementations and recent improvements in Gentry-type FHE schemes are also surveyed. Finally, further research directions are discussed. This survey is intended to give a clear knowledge and foundation to researchers and practitioners interested in knowing, applying, as well as extending the state of the art HE, PHE, SWHE, and FHE systems.Comment: - Updated. (October 6, 2017) - This paper is an early draft of the survey that is being submitted to ACM CSUR and has been uploaded to arXiv for feedback from stakeholder

An inquiry into the metrics for evaluation of localization algorithms in wireless ad hoc and sensor networks

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2008.Thesis (Master's) -- -Bilkent University, 2008.Includes bibliographical references leaves 65-69In ad-hoc and sensor networks, the location of a sensor node making an observation is a vital piece of information to allow accurate data analysis. GPS is an established technology to enable precise position information. Yet, resource constraints and size issues prohibit its use in small sensor nodes that are designed to be cost efficient. Instead, most positions are estimated by a number of algorithms. Such estimates, inevitably introduce errors in the information collected from the field, and it is very important to determine the error in cases where they lead to inaccurate data analysis. After all, many components of the application rely on the reported locations including decision making processes. It is, therefore, vital to understand the impact of errors from the applications’ point of view. To date, the focus on location estimation was on individual accuracy of each sensor’s position in isolation to the complete network. In this thesis, we point out the problems with such an approach that does not consider the complete network topology and the relative positions of nodes in comparison to each other. We then describe the existing metrics, which are used in the literature, and also propose some novel metrics that can be used in this area of research. Furthermore, we run simulations to understand the behavior of the existing and proposed metrics. After having discussed the simulation results, we suggest a metric selection methodology that can be used for wireless sensor network applications.Aksu, HidayetM.S

Efficient analysis of large-scale social networks using big-data platforms

Ankara : The Computer Engineering and The Graduate School of Engineering and Science of Bilkent University, 2014.Thesis (Ph. D.) -- Bilkent University, 2014.Includes bibliographical references leaves 133-145.In recent years, the rise of very large, rich content networks re-ignited interest to complex/social network analysis at the big data scale, which makes it possible to understand social interactions at large scale while it poses computation challenges to early works with algorithm complexity greater than O(n). This thesis analyzes social networks at very large-scales to derive important parameters and characteristics in an efficient and effective way using big-data platforms. With the popularization of mobile phone usage, telecommunication networks have turned into a socially binding medium and enables researches to analyze social interactions at very large scales. Degree distribution is one of the most important characteristics of social networks and to study degree characteristics and structural properties in large-scale social networks, in this thesis we first gathered a tera-scale dataset of telecommunication call detail records. Using this data we empirically evaluate some statistical models against the degree distribution of the country’s call graph and determine that a Pareto log-normal distribution provides the best fit, despite claims in the literature that power-law distribution is the best model. We also question and derive answers for how network operator, size, density and location affect degree distribution to understand the parameters governing it in social networks. Besides structural property analysis, community identification is of great interest in practice to learn high cohesive subnetworks about different subjects in a social network. In graph theory, k-core is a key metric used to identify subgraphs of high cohesion, also known as the ‘dense’ regions of a graph. As the real world graphs such as social network graphs grow in size, the contents get richer and the topologies change dynamically, we are challenged not only to materialize k-core subgraphs for one time but also to maintain them in order to keep up with continuous updates. These challenges inspired us to propose a new set of distributed algorithms for k-core view construction and maintenance on a horizontally scaling storage and computing platform. Experimental evaluation results demonstrated orders of magnitude speedup and advantages of maintaining k-core incrementally and in batch windows over complete reconstruction approaches. Moreover, the intensity of community engagement can be distinguished at multiple levels, resulting in a multiresolution community representation that has to be maintained over time. We also propose distributed algorithms to construct and maintain a multi-k-core graphs, implemented on the scalable big-data platform Apache HBase. Our experimental evaluation results demonstrate orders of magnitude speedup by maintaining multi-k-core incrementally over complete reconstruction. Furthermore, we propose a graph aware cache system designed for distributed graph processing. Experimental results demonstrate up to 15x speedup compared to traditional LRU based cache systems.Aksu, HidayetPh.D