k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data

Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed k-NN protocol protects the confidentiality of the data, user's input query, and data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our solution through various experiments.Comment: 29 pages, 2 figures, 3 tables arXiv admin note: substantial text overlap with arXiv:1307.482

Secure k-Nearest Neighbor Query over Encrypted Data in Outsourced Environments

For the past decade, query processing on relational data has been studied extensively, and many theoretical and practical solutions to query processing have been proposed under various scenarios. With the recent popularity of cloud computing, users now have the opportunity to outsource their data as well as the data management tasks to the cloud. However, due to the rise of various privacy issues, sensitive data (e.g., medical records) need to be encrypted before outsourcing to the cloud. In addition, query processing tasks should be handled by the cloud; otherwise, there would be no point to outsource the data at the first place. To process queries over encrypted data without the cloud ever decrypting the data is a very challenging task. In this paper, we focus on solving the k-nearest neighbor (kNN) query problem over encrypted database outsourced to a cloud: a user issues an encrypted query record to the cloud, and the cloud returns the k closest records to the user. We first present a basic scheme and demonstrate that such a naive solution is not secure. To provide better security, we propose a secure kNN protocol that protects the confidentiality of the data, user's input query, and data access patterns. Also, we empirically analyze the efficiency of our protocols through various experiments. These results indicate that our secure protocol is very efficient on the user end, and this lightweight scheme allows a user to use any mobile device to perform the kNN query.Comment: 23 pages, 8 figures, and 4 table

Secure Grouping Protocol Using a Deck of Cards

We consider a problem, which we call secure grouping, of dividing a number of parties into some subsets (groups) in the following manner: Each party has to know the other members of his/her group, while he/she may not know anything about how the remaining parties are divided (except for certain public predetermined constraints, such as the number of parties in each group). In this paper, we construct an information-theoretically secure protocol using a deck of physical cards to solve the problem, which is jointly executable by the parties themselves without a trusted third party. Despite the non-triviality and the potential usefulness of the secure grouping, our proposed protocol is fairly simple to describe and execute. Our protocol is based on algebraic properties of conjugate permutations. A key ingredient of our protocol is our new techniques to apply multiplication and inverse operations to hidden permutations (i.e., those encoded by using face-down cards), which would be of independent interest and would have various potential applications

Efficient cryptographic primitives: Secure comparison, binary decomposition and proxy re-encryption

”Data outsourcing becomes an essential paradigm for an organization to reduce operation costs on supporting and managing its IT infrastructure. When sensitive data are outsourced to a remote server, the data generally need to be encrypted before outsourcing. To preserve the confidentiality of the data, any computations performed by the server should only be on the encrypted data. In other words, the encrypted data should not be decrypted during any stage of the computation. This kind of task is commonly termed as query processing over encrypted data (QPED). One natural solution to solve the QPED problem is to utilize fully homomorphic encryption. However, fully homomorphic encryption is yet to be practical. The second solution is to adopt multi-server setting. However, the existing work is not efficient. Their implementations adopt costly primitives, such as secure comparison, binary decomposition among others, which reduce the efficiency of the whole protocols. Therefore, the improvement of these primitives results in high efficiency of the protocols. To have a well-defined scope, the following types of computations are considered: secure comparison (CMP), secure binary decomposition (SBD) and proxy re-encryption (PRE). We adopt the secret sharing scheme and paillier public key encryption as building blocks, and all computations can be done on the encrypted data by utilizing multiple servers. We analyze the security and the complexity of our proposed protocols, and their efficiencies are evaluated by comparing with the existing solutions.”--Abstract, page iii