Privacy Preserving Query over Encrypted Multidimensional Massive Data in Cloud Storage
XIANG Guangli, LIN Xiang, WANG Hao, LI Beilei1. College of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, Hubei, China; 2. Wuhan Union Hospital Affiliated to Tongji Medical Col-lege, Huazhong University of Science and Technology, Wuhan 430022, Hubei, China
Cloud storage is widely used in massive data outsourcing, but how to efficiently query encrypted multidimensional data stored in an untrusted cloud environment remains a research challenge. We propose a high performance and privacy-preserving query (pLSH-PPQ) scheme over encrypted multidimensional data to address this challenge. In our scheme, for a given query, the proxy server will return K top similar data object identifiers. An enhanced Ciphertext-Policy Attribute-Based Encryption (CP-ABE) policy is used to control access to the search results. Therefore, only the requester with the permission attribute can obtain correct secret keys to decrypt the data. Security analysis proves that the pLSH-PPQ scheme achieves data confidentiality and reserves the data owner’s privacy in a semi-trusted cloud. In addition, evaluations demonstrate that the pLSH-PPQ scheme can significantly reduce response time and provide high search efficiency without compromising on search quality.
Key words:cloud storage; privacy preservation; Locality Sensitive Hashing Scheme based on p-stable distributions (p-LSH); permission attribute
 Song D X, Wagner D, Perrig A. Practical techniques for searches on encrypted data[C]// Security and Privacy, 2000 S&P 2000 Proceedings 2000 IEEE Symposium on. Washington D C: IEEE, 2002:44-52.
 Goyal V, Pandey O, Sahai A, et al. Attribute-based encryption for fine-grained access control of encrypted data [C] // ACM Conference on Computer and Communications Security. New York: ACM, 2006: 89-98.
 Li J, Wang Q, Wang C, et al. Fuzzy keyword search over encrypted data in cloud computing[C]// INFOCOM, 2010 Proceedings IEEE. Piscataway: IEEE, 2010:1-5.
 Agrawal R, Kiernan J, Srikant R, et al. Order preserving encryption for numeric data[C]// ACM SIGMOD International Conference on Management of Data. New York: ACM, 2004: 563-574.
 Har-Peled S, Indyk P, Motwani R. Approximate nearest neighbor: Towards removing the curse of dimensionality[J]. Theory of Computing, 2000, (11): 604-613.
 Datar M, Immorlica N, Indyk P, et al. Locality-sensitive hashing scheme based on p-stable distributions[C]//Twentieth Symposium on Computational Geometry. New York: ACM, 2004: 253-262.
 Zhao Y W, Li B C. Object retrieval based on exact euclidean locality sensitive hashing[J]. Journal of Applied Sciences, 2012, 30(4): 349-355(Ch).
 Andoni A, Indyk P. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions[C]// IEEE Symposium on Foundations of Computer Science. Piscataway: IEEE, 2006:459-468.
 Motwani R, Naor A, Panigrahy R. Lower bounds on locality sensitive hashing[J]. Siam Journal on Discrete Mathematics, 2005, 21(4): 154-157.
 Bethencourt J, Sahai A, Waters B. Ciphertext-policy attribute-based encryption [C]// Symposium on Security and Privacy. Piscataway: IEEE, 2007: 321-334.
 Nolan J P. Stable Distributions: Models for Heavy-Tailed Data[M]. Boston: Birkhauser, 2005.
 Zolotarev V M, One-Dimensional Stable Distributions[M]. Washington: Mathematical Society, 1992.