Final Year Projects | IEEE Projects | Application Projects

---

• A Privacy-Preserving Location Monitoring System for Wireless Sensor Networks
• Rumor Riding: Anonymizing Unstructured Peer-to-Peer Systems
• Nymble: Blocking Misbehaving Users in Anonymizing Networks
• Data Leakage Detection

A Privacy-Preserving Location Monitoring System for Wireless Sensor Networks Posted: 27 May 2011 02:27 AM PDT A Privacy-Preserving Location Monitoring System for Wireless Sensor Networks Monitoring personal locations with a potentially untrusted server poses privacy threats to the monitored individuals. To this end, we propose a privacy-preserving location monitoring system for wireless sensor networks. In our system, we design two in-network location anonymization algorithms, namely, resource and quality-aware algorithms, that aim to enable the system to provide high-quality location monitoring services for system users, while preserving personal location privacy. Both algorithms rely on the well-established k-anonymity privacy concept, that is, a person is indistinguishable among k persons, to enable trusted sensor nodes to provide the aggregate location information of monitored persons for our system. Each aggregate location is in a form of a monitored area A along with the number of monitored persons residing in A, where A contains at least k persons. The resource-aware algorithm aims to minimize communication and computational cost, while the quality-aware algorithm aims to maximize the accuracy of the aggregate locations by minimizing their monitored areas. To utilize the aggregate location information to provide location monitoring services, we use a spatial histogram approach that estimates the distribution of the monitored persons based on the gathered aggregate location information. Then, the estimated distribution is used to provide location monitoring services through answering range queries. We evaluate our system through simulated experiments. The results show that our system provides high-quality location monitoring services for system users and guarantees the location privacy of the monitored persons.

Rumor Riding: Anonymizing Unstructured Peer-to-Peer Systems Posted: 27 May 2011 02:26 AM PDT Rumor Riding: Anonymizing Unstructured Peer-to-Peer Systems Although anonymizing Peer-to-Peer (P2P) systems often incurs extra traffic costs, many systems try to mask the identities of their users for privacy considerations. Existing anonymity approaches are mainly path-based: peers have to pre-construct an anonymous path before transmission. The overhead of maintaining and updating such paths is significantly high. We propose Rumor Riding (RR), a lightweight and non-path-based mutual anonymity protocol for decentralized P2P systems. Employing a random walk mechanism, RR takes advantage of lower overhead by mainly using the symmetric cryptographic algorithm. We conduct comprehensive trace-driven simulations to evaluate the effectiveness and efficiency of this design, and compare it with previous approaches. We also introduce some early experiences on RR implementations.

Nymble: Blocking Misbehaving Users in Anonymizing Networks Posted: 27 May 2011 02:25 AM PDT Nymble: Blocking Misbehaving Users in Anonymizing Networks Anonymizing networks such as Tor allow users to access Internet services privately by using a series of routers to hide the client’s IP address from the server. The success of such networks, however, has been limited by users employing this anonymity for abusive purposes such as defacing popular Web sites. Web site administrators routinely rely on IP-address blocking for disabling access to misbehaving users, but blocking IP addresses is not practical if the abuser routes through an anonymizing network. As a result, administrators block all known exit nodes of anonymizing networks, denying anonymous access to misbehaving and behaving users alike. To address this problem, we present Nymble, a system in which servers can "blacklist" misbehaving users, thereby blocking users without compromising their anonymity. Our system is thus agnostic to different servers’ definitions of misbehavior-servers can blacklist users for whatever reason, and the privacy of blacklisted users is maintained.

Data Leakage Detection Posted: 27 May 2011 02:25 AM PDT Data Leakage Detection We study the following problem: A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). Some of the data are leaked and found in an unauthorized place (e.g., on the web or somebody’s laptop). The distributor must assess the likelihood that the leaked data came from one or more agents, as opposed to having been independently gathered by other means. We propose data allocation strategies (across the agents) that improve the probability of identifying leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In some cases, we can also inject "realistic but fake" data records to further improve our chances of detecting leakage and identifying the guilty party.

You are subscribed to email updates from IEEE 2011 Projects, NS2 Projects, Final Year Projects To stop receiving these emails, you may unsubscribe now.	Email delivery powered by Google
Google Inc., 20 West Kinzie, Chicago IL USA 60610