Flexible fair and collusion resistant pseudonym providing system

In service providing systems, user authentication is required for different purposes such as billing, restricting unauthorized access, etc., to protect the privacy of users, their real identities should not be linked to the services that they use during authentication. A good solution is to use pseudonyms as temporary identities. On the other hand, it may also be required to have a backdoor in pseudonym systems for identity revealing that can be used by law enforcement agencies for legal reasons. Existing systems that retain a backdoor are either punitive (full user anonymity is revealed), or they are restrictive by revealing only current pseudonym identity of. In addition to that, existing systems are designed for a particular service and may not fit into others. In this paper, we address this gap and we propose a novel pseudonym providing and management system. Our system is flexible and can be tuned to fit into services for different service providers. The system is privacy-preserving and guarantees a level of anonymity for a particular number of users. Trust in our system is distributed among all system entities instead of centralizing it into a single trusted third party. More importantly, our system is highly resistant to collusions among the trusted entities. Our system also has the ability to reveal user identity fairly in case of a request by law enforcement. Analytical and simulation based performance evaluation showed that Collusion Resistant Pseudonym Providing System (CoRPPS) provides high level of anonymity with strong resistance against collusion attacks

Exploring Privacy Preservation in Outsourced K-Nearest Neighbors with Multiple Data Owners

The k-nearest neighbors (k-NN) algorithm is a popular and effective classification algorithm. Due to its large storage and computational requirements, it is suitable for cloud outsourcing. However, k-NN is often run on sensitive data such as medical records, user images, or personal information. It is important to protect the privacy of data in an outsourced k-NN system. Prior works have all assumed the data owners (who submit data to the outsourced k-NN system) are a single trusted party. However, we observe that in many practical scenarios, there may be multiple mutually distrusting data owners. In this work, we present the first framing and exploration of privacy preservation in an outsourced k-NN system with multiple data owners. We consider the various threat models introduced by this modification. We discover that under a particularly practical threat model that covers numerous scenarios, there exists a set of adaptive attacks that breach the data privacy of any exact k-NN system. The vulnerability is a result of the mathematical properties of k-NN and its output. Thus, we propose a privacy-preserving alternative system supporting kernel density estimation using a Gaussian kernel, a classification algorithm from the same family as k-NN. In many applications, this similar algorithm serves as a good substitute for k-NN. We additionally investigate solutions for other threat models, often through extensions on prior single data owner systems