An Open Environment (OE) is an electronic domain in which multiple entities need to interact but do not necessarily have complete knowledge of each other. In this setting, OC (Open Collaboration), a tool being developed to support a variety of electronic collaboration needs, may be useful. OC is built on the open-source JXTA toolkits. Group and role information is propagated in a peer-to-peer fashion, and peers can share files to any peer who is a member of an appropriate group or role. Detail...

Secure Content Exchange Negotiation System

Fig.1 Localization	Fig.2 GDL protocol/algorithm

Localization (Fig.1) provides fundamental support for location-aware applications and protocols in wireless sensor networks with mobile and static nodes.
We have developed methods developed a Geographic Distributed Localization (GDL) protocol/algorithm (Fig.2) with the following components: (i) Measurement Technique, (ii) Static Localization, (iii) Mobile Localization, (vi) Protocol Interface. which supports both static and mobile node localization with new sensor node model and new computational model for localization. There are many potential applications for GDL in security, location-aware applications, protocols.

Privacy in Sensor Networks

Sensor networks are used in many realtime applications for collecting information from monitored environments and objects, such as vehicle tracking, battlefield reconnaissance, and habitat monitoring. Following the increasingly wide deployment of sensor networks, privacy concerns have emerged as an obstacle. Source location protection in sensor networks becomes a very important problem when a sensor network is used in monitoring valuable assets or the source is a sensitive object.We propose a new concept, the cyclic entrapment method (CEM), to preserve the performance advantage of shortest path routing while also protecting the location of a source.

Information overload has become a problem in data sharing and collaboration: the amount of information users must sift through has reached the point where it is overwhelming. Recommender systems help people deal with information overload and provide them with personalized recommendations, content, and services. Because recommender systems are dependent on external sources of information, they are vulnerable to recommendation attacks. In these attacks, attackers influence a recommender system in a manner advantageous to them by introducing biased ratings. We have developed a series of approaches to detect a diverse and general set of recommendation attacks and demonstrate their effectiveness with data.