Privacy-Preserving Updates to Anonymous and Confidential Database
Thooyamani K.P, Velu Khanaa, M.R.Arun Venkatesh · International Journal of Data Mining Techniques and Applications · 2012
The current trend in the application space towards systems of loosely coupled and dynamically bound components that enables just-in-time integration jeopardizes the security of information that is shared between the broker, the requester, and the provider at runtime. In particular, new advances in data mining and knowledge discovery that allow for the extraction of hidden knowledge in an enormous amount of data impose new threats on the seamless integration of information. We consider the problem of building privacy preserving algorithms for one category of data mining techniques, association rule mining. Suppose Alice owns a k-anonymous database and needs to determine whether her database, when inserted with a tuple owned by Bob, is still k-anonymous. Also, suppose that access to the database is strictly controlled, because for example data are used for certain experiments that need to be maintained confidential. Clearly, allowing Alice to directly read the contents of the tuple breaks the privacy of Bob (e.g., a patient’s medical record); on the other hand, the confidentiality of the database managed by Alice is violated once Bob has access to the contents of the database. Thus, the problem is to check whether the database inserted with the tuple is still k-anonymous, without letting Alice and Bob know the contents of the tuple and the database, respectively. In this paper, we propose two protocols solving this problem on suppression-based and generalization-based k-anonymous and confidential databases. The protocols rely on well-known cryptographic assumptions, and we provide theoretical analyses to proof their soundness and experimental results to illustrate their efficiency. We have presented two secure protocols for privately checking whether a k-anonymous database retains its anonymity once a new tuple is being inserted to it. Since the proposed protocols ensure the updated database remains K-anonymous, the results returned from a user’s (or a medical researcher’s) query are also k-anonymous. Thus, the patient or the data provider’s privacy cannot be violated from any query. As long as the database is updated properly using the proposed protocols, the user queries under our application domain are always privacypreserving.