OBJECTIVE: To determine what, if any, opportunity exists in using administrative medical claims data for supplemental reporting to the state infectious disease registry system. MATERIALS AND METHODS: Cases of five tick-borne (Lyme disease (LD), babesiosis, ehrlichiosis, Rocky Mountain spotted fever (RMSF), tularemia) and two mosquito-borne diseases (West Nile virus, La Crosse viral encephalitis) reported to the Tennessee Department of Health during 2000-2009 were selected for study. Similarly, medically diagnosed cases from a Tennessee-based managed care organization (MCO) claims data warehouse were extracted for the same time period. MCO and Tennessee Department of Health incidence rates were compared using a complete randomized block design within a general linear mixed model to measure potential supplemental reporting opportunity. RESULTS: MCO LD incidence was 7.7 times higher (p<0.001) than that reported to the state, possibly indicating significant under-reporting (∼196 unreported cases per year). MCO data also suggest about 33 cases of RMSF go unreported each year in Tennessee (p<0.001). Three cases of babesiosis were discovered using claims data, a significant finding as this disease was only recently confirmed in Tennessee. DISCUSSION: Data sharing between MCOs and health departments for vaccine information already exists (eg, the Vaccine Safety Datalink Rapid Cycle Analysis project). There may be a significant opportunity in Tennessee to supplement the current passive infectious disease reporting system with administrative claims data, particularly for LD and RMSF. CONCLUSIONS: There are limitations with administrative claims data, but health plans may help bridge data gaps and support the federal administration's vision of combining public and private data into one source.

Many reputation management systems have been developed under the assumption that each entity in the system will use a variant of the same scoring function. Much of the previous work in reputation management has focused on providing robustness and improving performance for a given reputation scheme. In this paper, we present a reputation-based trust management framework that supports the synthesis of trust-related feedback from many different entities while also providing each entity with the flexibility to apply different scoring functions over the same feedback data for customized trust evaluations. We also propose a novel scheme to cache trust values based on recent client activity. To evaluate our approach, we implemented our trust management service and tested it on a realistic application scenario in both LAN and WAN distributed environments. Our results indicate that our trust management service can effectively support multiple scoring functions with low overhead and high availability.

As Internet telephony systems continue to replace existing Public Switched Telephone Network systems, proxy servers running the Session Initiation Protocol (SIP) will continue to grow in importance for Voice-over-IP deployments that use SIP for call signaling. Since the protection of the global telecommunications infrastructure is critical to people's everyday lives, ensuring the availability of SIP proxy servers under attack should be a high priority. This paper first describes a disruptive denial-of-service attack that exploits the semantics of the SIP protocol to exhaust resources at a stateful SIP proxy server. Unlike previous approaches that focus on flooding-based denial-of-service attacks, we consider attacks that do not result in high incoming call traffic rates at the SIP proxy server. After describing this semantic-based attack, we then propose a new algorithm to reduce the effects of such an attack. Our algorithm has been implemented in a SIP proxy server and evaluated extensively through experiments on a local testbed.

Large-scale distributed systems, e.g., grid or P2P networks, are targets for large-scale attacks. Unfortunately, few existing systems support propagation of alerts during the attack itself while also suppressing disruptive alerts from faulty or malicious sources. This paper proposes the "ContagAlert" protocol, which uses contagion spreading behavior to spread alerts. ContagAlert rapidly propagates alerts during attacks while also suppressing disruptive alerts. The core contagion protocols in the system are completely localized, but result in desired behavior at the network scale. We analyze and evaluate our protocol with synthetic simulations and in both Internet worm and DoS attack scenarios