Ioannis G. Nikolakopoulos

Exploiting semantic proximities for content search over p2p networks

In this paper, we address the issue of content search over peer-to peer networks. We use the concept of semantic proximity that exploits the commonalities of interests exhibited among peer users so as to decompose the network into semantic clusters. We initially define search entropy, as a metric indicating the average number of packets required to locate the requested content. Then, spectral clustering is used to organize the peer nodes into semantic clusters so that (a) the probability that a node locates content within its own cluster is maximized, while simultaneously; (b) the respective probability of finding this content outside this cluster is minimized. The proposed semantic partitioning algorithm is then extended into a hierarchical two-tier scheme, in which practical issues arising for the deployment of a peer-to-peer (p2p) application can be more easily addressed. After the system has been initialized, a dynamic algorithm places new users that join the p2p network into appropriately selected clusters and also handles peer departures without the need for matrix eigen decomposition process which is necessary for the assessment of the initial static partitioning. Our experimental results validate that (a) our static partitioning outperforms traditional and novel search techniques and (b) our dynamic algorithm is able to efficiently track the systems progression maintaining the search entropy close to the initially assessed levels.

Optimal decomposition of P2P networks based on file exchange patterns for multimedia content search & replication

In this paper, we address the issues of multimedia content search and replication over peer-to peer networks. We use the concept of semantic proximity that exploits the file exchange patterns exhibited among peer users so as to decompose the network into semantic clusters. Peer nodes are then decomposed into semantic clusters so that a) the probability that a node locates content within its own cluster is maximized, while simultaneously b) the respective probability of finding this content outside this cluster is minimized The semantic organization is then used for applying efficient cluster-based content replication strategies. Two different schemes are examined; the unrestricted and the restricted approach. The first one distributes multimedia content within peers of a cluster so that the average hop distance among nodes and objects weighted by the object popularity is minimized. This approach takes into account no Quality of Service (QoS) guarantees. Instead, the second scheme iteratively partitions the nodes of a cluster during each object replication based on a QoS violation criterion. The proposed algorithms are experimentally evaluated and compared with other approaches to demonstrate the efficacy of the proposed schemes.

Privacy and resource protection in personal network federations

The emerging need for peer to peer communication in multi-user environments as well as the breakthrough of ubiquitous computing have contributed to the evolution of Personal Networking towards multiple network collaboration, namely the Personal Networks Federations. Nevertheless, in these environments new threat issues arise, calling for appropriate countermeasures. In this paper, the authors propose a framework for privacy protection in personal networking, based on the idea of creating a trust management mechanism that will meet the need for protecting privacy and safeguarding sensitive and personal information, while satisfying the need for validating and authorizing users that have access to personal resources.

Trust and security in Personal Network environments

The rapid growth of personal communications has increased the need for user interaction and communication in a Peer-to-Peer (P2P) fashion together with the need for protection against security threats and the corresponding need for trust establishment schemes. In this paper, the concept of Personal Network Federation (PN-F) is discussed, and the related security and privacy threats are presented together with solutions taken out of the academic bibliography and research project results. The paper concludes with a reference to the need for privacy protection as a counterforce for complete release of personal information in order to support true personalised service provision.

PLASMA: personalized, location aware services over mobile architectures

In this paper, we describe a platform that aims to provide a service framework that can be deployed over mobile devices (smart phones, PDAs, PocketPCs) offering personalized, context aware (with emphasis on location awareness) services. The services, instead of being offered in the traditional way, are based on an open architecture, which allows the user to design, test, deploy and share service modules. The framework allows for personalized deployment of services that can be customized as regards the level of privacy of personal information and accuracy of offered data.