As more corporate and private users outsource their data to cloud storage, recent data breach incidents make end-to-end encryption increasingly desirable. Unfortunately, semantically secure encryption renders various cost-effective storage optimization techniques, such as data deduplication, ineffective. On this ground Stanek et al. [1] introduced the concept of “data popularity” arguing that data known/owned by many users do not require as strong protection as unpopular data; based on this, Stanek et al. presented an encryption scheme, where the initially semantically secure ciphertext of a file is transparently downgraded to a convergent ciphertext that allows for deduplication as soon as the file becomes popular. In this paper we propose an enhanced version of the original scheme. Focusing on practicality, we modify the original scheme to improve its efficiency and emphasize clear functionality. We analyze the efficiency based on popularity properties of real datasets and provide a detailed performance evaluation, including comparison to alternative schemes in real-like settings. Importantly, the new scheme moves the handling of sensitive decryption shares and popularity state information out of the cloud storage, allowing for improved security notion, simpler security proofs and easier adoption. We show that the new scheme is secure under the Symmetric External Diffie-Hellman assumption in the random oracle model.

The Session Initiation Protocol (SIP) is a signaling protocol widely used nowadays for controlling multimedia communication sessions. Thus, understanding and troubleshooting SIP behavior is of utmost importance to network designers and operators. However, SIP traffic traces are hard to come by due to privacy and confidentiality issues. SIP contains a lot of personal information spread within the various SIP messages — IP addresses, names, usernames and domains, e-mail addresses etc. The known IP-address anonymization methods are thus insufficient. We present SiAnTo, an extended anonymization technique that substitutes session-participant information with matching, but nondescript, labels. This allows for SIP traces to be publicly shared, while keeping interesting traffic-session properties intact. We further demonstrate its usefulness by studying the problem of SIP NAT traversal as recorded in the anonymized traces. We analyze properties of the so-called “registration storm” incident and measure the influence of the active NAT traversal techniques on SIP traffic pattern, both only possible thanks to the preservation of session relationships inside the anonymized traces. As further benefit to the research community, we set up a public data-store with both the anonymization module and the anonymized traces available and invite other parties to share further SIP data using these open tools.

As Voice-over-IP becomes a commonly used technology, the need to keep it secure and reliable has grown. Session Initiation Protocol (SIP) is most often used to deploy VoIP and therefore SIP servers, the base components of SIP, are the most obvious targets of potential attacks. It has been demonstrated, that SIP servers are highly prone to DDoS flood attacks, yet no generally accepted defense solution mitigating these attacks is available. We propose a novel defense architecture against SIP DDoS floods, based upon a redirection mechanism and a combination of source and destination traffic filtering, exploiting the combined advantage of all the three techniques. We show that the proposed solution effectively mitigates various types of SIP DDoS flood attacks, discuss its strengths and weaknesses and propose its potential usability for other protocols. We also provide results of performance evaluation of the defense solution deployed in a SIP testbed.

With the growing popularity of Voice-over-IP communication and of the SIP protocol, mobile networks including, denial-of-service attacks against the signaling are an increasingly menacing threat. We present SIPp-DD, a tool for generating real-like SIP DDoS flood attacks. SIPp-DD modifies the popular SIPp call generator and offers the option to spoof source IP addresses and ports of the generated messages. For flexibility, any set of source IP addresses and ports can be input, using a text file. To create real-like attacks, we analyze some of the publicly available DDoS flood attacks, derive typical distributions of address and packet populations and employ those in attack generation. We compare the generator outputs with the real analyzed DDoS floods and demonstrate the tool applicability by performing a DDoS attack within a real SIP-server testbed.

OBJECTIVE: This paper presents a model for analysis of chronic disease prescribing action over time in terms of transitions in status of therapy as indicated in electronic prescribing records. The quality of alerts derived from these therapeutic state transitions is assessed in the context of antihypertensive prescribing. DESIGN: A set of alert criteria is developed based on analysis of state-transition in past antihypertensive prescribing of a rural Australian General Practice. Thirty active patients coded as hypertensive with alerts on six months of previously un-reviewed prescribing, and 30 hypertensive patients without alerts, are randomly sampled and independently reviewed by the practice's two main general practice physicians (GPs), each GP reviewing 20 alert and 20 non-alert cases (providing 10 alert and 10 non-alert cases for agreement assessment). MEASUREMENTS: GPs provide blind assessment of quality of hypertension management and retrospective assessment of alert relevance. RESULTS: Alerts were found on 66 of 611 cases with coded hypertension with 37 alerts on the 30 sampled alert cases. GPs assessed alerting sensitivity as 74% (CI 52% - 89%) and specificity as 61% (CI 45% - 74%) for the sample, which is estimated as 26% sensitivity and 93% specificity for the antihypertensive population. Agreement between the GPs on assessment of alert relevance was fair (kappa = 0.37). CONCLUSIONS: Data-driven development of alerts from electronic prescribing records using analysis of therapeutic state transition shows promise for derivation of high-specificity alerts to improve the quality of chronic disease management activities.

Although genetic services have potential value for health care and disease control, there is no systematic solution for knowledge capture in Medical Genetic Testing (MGT) Laboratories. This paper addresses such knowledge management (KM) technology weakness in genetics domain, then proposes an information system (IS) architecture to establish process automation and content management of the distributed workflow of knowledge generation and knowledge management (KG&KM) during MGT result interpretation. The presented IS will validate the interpretation decision by using information systems/information technologies (IS/IT), esp. KM tools, such as workflow management system (WfMS), search engine and groupware. Once developed and implemented, our integrated system will significantly improve MGT lab researchers' KG&KM performance through increasing knowledge capture, improving documentation quality and maintaining (if not improving) users' information satisfaction