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This paper focus on providing a secure and trustworthy solution for virtual machine (VM) migration within an existing cloud provider domain, and/or to the other federating cloud providers. The infr ...

Distance-bounding anonymous credentials could be used for any location proofs that do not need to identify the prover and thus could make even notoriously invasive mechanisms such as location-based services privacy-preserving. There is, however, no secure distance-bounding protocol for general attribute-based anonymous credentials. Brands and Chaum’s (EUROCRYPT’93) protocol combining distance-bounding and Schnorr identification comes close, but does not fulfill the requirements of modern distance-bounding protocols. For that, we need a secure distance-bounding zero-knowledge proof-of-knowledge resisting mafia fraud, distance fraud, distance hijacking and terrorist fraud.

Popular social and e-commerce sites increasingly rely on crowd computing to rate and rank content, users, products and businesses. Today, attackers who create fake (Sybil) identities can easily tamper with these computations. Existing defenses that largely focus on detecting individual Sybil identities have a fundamental limitation: Adaptive attackers can create hard-to-detect Sybil identities to tamper arbitrary crowd computations. In this paper, we propose Stamper, an approach for detecting tampered crowd computations that significantly raises the bar for evasion by adaptive attackers. Stamper design is based on two key insights: First, Sybil attack detection gains strength in numbers: we propose statistical analysis techniques that can determine if a large crowd computation has been tampered by Sybils, even when it is fundamentally hard to infer which of the participating identities are Sybil. Second, Sybil identities cannot forge the timestamps of their activities as they are recorded by system operators; Stamper analyzes these unforgeable timestamps to foil adaptive attackers. We applied Stamper to detect tampered computations in Yelp and Twitter. We not only detected previously known tampered computations with high accuracy, but also uncovered tens of thousands of previously unknown tampered computations in these systems.