Mgr. Jan Staněk, Ph.D.

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.

As more corporate and private users outsource their data to cloud storage providers, recent data breach incidents make end-to-end encryption an increasingly prominent requirement. Unfortunately, semantically secure encryption schemes render various cost-eective stor- age optimization techniques, such as data deduplication, ineective. We present a novel idea that dierentiates data according to their popular- ity. Based on this idea, we design an encryption scheme that guarantees semantic security for unpopular data and provides weaker security and better storage and bandwidth benets for popular data. This way, data deduplication can be eective for popular data, whilst semantically secure encryption protects unpopular content. We show that our scheme is secure under the Symmetric External Decisional 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.

Voice-over-IP (VoIP) is currently one of the most commonly used communication options and Session Initiation Protocol (SIP) is most often used for VoIP deployment. However, there is not a lot of general knowledge about typical SIP traffic and behavior and research work in this area largely works with various assumptions. To address this deficiency, we present a thorough study of traffic of a real, free and publicly open SIP server. The findings reveal, among others, a surprisingly high overhead of SIP due to connection maintenance through NAT nodes, differences from typical HTTP Power-law patterns and various unexpected creative uses of SIP servers for commercial services. We also discuss the (un)suitability of SIP deployment into a cloud environment.

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.