Konstantinos Chatzikokolakis

Toward spectrum sharing: opportunities and technical enablers

The vast increase in the number of mobile devices and their mobile traffic demands indicates the need for additional spectrum for cellular communications. Since it is not trivial to allocate exclusively new spectrum bands for cellular communications, it is imperative to improve the spectrum usage through new spectrum sharing mechanisms. This implies that the mobile network operators will have to cooperate and interact to cover the augmented traffic requirements. In this article we present a novel architectural framework that enables the mobile network operators and other spectrum license holders to exchange information about spectrum availability. We also present a novel spectrum sharing mechanism based on fuzzy logic to facilitate operators in selecting the most suitable spectrum to cover their needs.

Constructing elastic distinguishability metrics for location privacy

Abstract With the increasing popularity of hand-held devices, location-based applications and services have access to accurate and real-time location information, raising serious privacy concerns for their users. The recently introduced notion of geo-indistinguishability tries to address this problem by adapting the well-known concept of differential privacy to the area of location-based systems. Although geo-indistinguishability presents various appealing aspects, it has the problem of treating space in a uniform way, imposing the addition of the same amount of noise everywhere on the map. In this paper we propose a novel elastic distinguishability metric that warps the geometrical distance, capturing the different degrees of density of each area. As a consequence, the obtained mechanism adapts the level of noise while achieving the same degree of privacy everywhere. We also show how such an elastic metric can easily incorporate the concept of a “geographic fence” that is commonly employed to protect the highly recurrent locations of a user, such as his home or work. We perform an extensive evaluation of our technique by building an elastic metric for Paris’ wide metropolitan area, using semantic information from the OpenStreetMap database. We compare the resulting mechanism against the Planar Laplace mechanism satisfying standard geo-indistinguishability, using two real-world datasets from the Gowalla and Brightkite location-based social networks. The results show that the elastic mechanism adapts well to the semantics of each area, adjusting the noise as we move outside the city center, hence offering better overall privacy.1

Efficient Utility Improvement for Location Privacy

Abstract Sensitive information is present on our phones, disks, watches and computers. Its protection is essential. Plausible deniability of stored data allows individuals to deny that their device contains a piece of sensitive information. This constitutes a key tool in the fight against oppressive governments and censorship. Unfortunately, existing solutions, such as the now defunct TrueCrypt [5], can defend only against an adversary that can access a user’s device at most once (“single-snapshot adversary”). Recent solutions have traded significant performance overheads for the ability to handle more powerful adversaries able to access the device at multiple points in time (“multi-snapshot adversary”). In this paper we show that this sacrifice is not necessary. We introduce and build DataLair1, a practical plausible deniability mechanism. When compared with existing approaches, DataLair is two orders of magnitude faster for public data accesses, and 5 times faster for hidden data accesses. An important component in DataLair is a new write-only ORAM construction which improves on the complexity of the state of the art write-only ORAM by a factor of O(logN), where N denotes the underlying storage disk size.

Construction and Repair: A Hybrid Approach to Search in CSPs

In order to obtain a solution to a constraint satisfaction problem, constructive methods iteratively extend a consistent partial assignment until all problem variables are instantiated. If the current partial assignment is proved to be inconsistent, it is then necessary to backtrack and perform alternative instantiations. On the other hand, reparative methods iteratively repair an inconsistent complete assignment until it becomes consistent. In this research, we investigate an approach which allows for the combination of constructive and reparative methods, in the hope of exploiting their intrinsic advantages and circumventing their shortcomings. Initially, we discuss a general hybrid method called cr and then proceed to specify its parameters in order to provide a fully operational search method called cnr. The reparative stage therein is of particular interest: we employ techniques borrowed from local search and propose a general cost function for evaluating partial assignments. In addition, we present experimental results on the open-shop scheduling problem. The new method is compared against specialized algorithms and exhibits outstanding performance, yielding solutions of high quality and even improving the best known solution to a number of instances.

Using SDN as a key enabler for co-primary spectrum sharing

Over the past years the mobile traffic demands and the number of the connected devices have been constantly increasing thus indicating that in the near future the current spectrum usage models may fail to cover user needs. At the same time, spectrum scarcity over several Mobile Network Operators (MNOs) leads to inefficient spectrum usage. Co-primary spectrum sharing is a scheme for trading resources among MNOs, enabling flexible spectrum usage, which is arising as a possible solution to spectrum scarcity. However, up to now the schemes for co-primary shared access are hard to be implemented. The recently introduced concept of Software Defined Networking (SDN) gives the flexibility to MNOs network configuration and, therefore, is arising as an enabler for efficiently handling spectrum trading. In this paper we present a complete functional architecture aiming at enabling co-primary spectrum sharing among MNOs. We, furthermore, propose a scheme for implementing the introduced architectural scheme, coupled with fully described information flows among the (SDN) network entities. Such solution enables the concurrent reconfiguration (when required) for both core and access network nodes, based on the spectrum trading process.

On the Way to Massive Access in 5G: Challenges and Solutions for Massive Machine Communications

Machine Type Communication (MTC) is expected to play a significant role in fifth generation (5G) wireless and mobile communication systems. The requirements of such type of communication mainly focus on scalability (i.e., number of supported end-devices) and timing issues. Since existing cellular systems were not designed to support such vast number of devices, it is expected that they will throttle the limited network resources. In this paper, we introduce an effective solution for handling the signalling bottlenecks caused by massive machine communications in future 5G systems. The proposed approach is based on a device classification scheme using the devices’ requirements and position for forming groups of devices with the same or similar device characteristics. Our scheme is analysed, and the evaluation results indicate that the proposed solution yields significant reduction in collisions compared to the standard when MTC devices attempt to access the Random Access CHannel (RACH).

Context-Aware Location Management of Groups of Devices in 5G Networks

Location Management LM is an important function of mobile cellular networks that enables network to locate the users. Mechanisms applicable in legacy systems are not able to cope with the vast increase of devices and the strict communication requirements expected in 5G networks. In this paper we propose a novel scheme for LM that exploits mobility context of User Equipments UEs, keeps track of their location with high accuracy and pages small number of cells when incoming calls arrive. The analysis shows that Location Management is significantly benefitted from the proposed mechanisms.

Implementing clustering for vehicular ad-hoc networks in ns-3

Vehicular Ad-Hoc Networks are of great interest for secure, efficient and reliable communications, in the last years. In this paper, we firstly present an implementation of a VANET algorithm to form clusters in ns-3 simulation environment. We provide the details of the algorithm and the communication messages exchanged among vehicles to form and maintain clusters of moving nodes. Then, we present an application scenario for safety and emergency situations. Vehicles form clusters using the proposed algorithm while the application calculates safety messages statistics.

A Logical Characterization of Differential Privacy via Behavioral Metrics

Differential privacy is a formal definition of privacy ensuring that sensitive information relative to individuals cannot be inferred by querying a database. In this paper, we exploit a modeling of this framework via labeled Markov Chains (LMCs) to provide a logical characterization of differential privacy: we consider a probabilistic variant of the Hennessy-Milner logic and we define a syntactical distance on formulae in it measuring their syntactic disparities. Then, we define a trace distance on LMCs in terms of the syntactic distance between the sets of formulae satisfied by them. We prove that such distance corresponds to the level of privacy of the LMCs. Moreover, we use the distance on formulae to define a real-valued semantics for them, from which we obtain a logical characterization of weak anonymity: the level of anonymity is measured in terms of the smallest formula distinguishing the considered LMCs. Then, we focus on bisimulation semantics on nondeterministic probabilistic processes and we provide a logical characterization of generalized bisimulation metrics, namely those defined via the generalized Kantorovich lifting. Our characterization is based on the notion of mimicking formula of a process and the syntactic distance on formulae, where the former captures the observable behavior of the corresponding process and allows us to characterize bisimilarity. We show that the generalized bisimulation distance on processes is equal to the syntactic distance on their mimicking formulae. Moreover, we use the distance on mimicking formulae to obtain bounds on differential privacy.

Up-To Techniques for Generalized Bisimulation Metrics

Bisimulation metrics allow us to compute distances between the behaviors of probabilistic systems. In this paper we present enhancements of the proof method based on bisimulation metrics, by extending the theory of up-to techniques to (pre)metrics on discrete probabilistic concurrent processes. n nUp-to techniques have proved to be a powerful proof method for showing that two systems are bisimilar, since they make it possible to build (and thereby check) smaller relations in bisimulation proofs. We define soundness conditions for up-to techniques on metrics, and study compatibility properties that allow us to safely compose up-to techniques with each other. As an example, we derive the soundness of the up-to-bisimilarity-metric-and-context technique. n nThe study is carried out for a generalized version of the bisimulation metrics, in which the Kantorovich lifting is parametrized with respect to a distance function. The standard bisimulation metrics, as well as metrics aimed at capturing multiplicative properties such as differential privacy, are specific instances of this general definition.