SGPM: a privacy protected approach of timeconstrained graph pattern matching in cloud Graph pattern matching (GPM) is an important operation on graph computation. Most existing work assumes that query graph or data graph is static, which is contrary to the fact that graphs in real life are intrinsically dynamic. Therefore, timeconstrained graph pattern matching has been introduced. However, querying timeconstrained graph pattern in a temporal graph is not an easy work because of the high computation complexity. Query outsourcing, with the help of cloud computing, is adopted in this paper. Outsourcing of data storage and query becomes increasingly popular due to the prevalence of cloud computing. However, sensitive data need to be encrypted before outsourcing for various privacy concerns. To execute queries over encrypted data is a very challenging problem and has received much attention recently. However, most existing approaches only support limited kinds of queries, moreover, they cannot be completely outsourced. In this paper, a Somewhat Homomorphic Encryption (mathcal SmathscrHmathcal E) approach is adopted to design a protocol which enables general queries on encrypted data and query outsourcing as well. The key issue in the paper is to (1) enable the query provider to filter data rows with homomorphic encrypted result of comparison operators; (2) completely relieve the data owner of partaking of the process of queries. An effective dualcloud protocol (DCP) which enables the cloud to understand the result of comparisons though homomorphic encrypted values is proposed. Moreover, The efficiency of the baseline approach is greatly improved by packing and GPUaccelerating technologies. Experimental study shows that the optimized approach outperforms the baseline approach and the recently reported similar approaches.

Efficient twoparty privacypreserving collaborative kmeans clustering protocol supporting both storage and computation outsourcing Abstract   Nowadays, cloud computing has developed well and been applied in many kinds of areas. However, privacy is still the most challenging problem which obstructs it being applied in some privacysensitive fields, such as finance and government. Advanced cryptographic algorithms provide data privacy with encryption, which can also support computation on such encrypted data. However, new challenge arises when such ciphertexts come from different parties. In particular, how to execute collaboratively data mining on encrypted data coming from different parties is a key issue from cloud service point of view. This paper focuses on privacy problem on outsourced kmeans clustering scheme for two parties. In particular, each partyxe2x80x99s data are encrypted only once and then stored in cloud. The proposed privacypreserving kmeans collaborative clustering protocol is executed mainly at the cloud, with     O  (  k  (  m    n  )  )     rounds of interactions among the two parties and the cloud, where m and n represent the total numbers of records for the two parties, respectively. It is shown that the protocol is secure in the semihonest security model and in the malicious model in which only one party is corrupted during the process of centroids recomputation. Both theoretical and experimental analysis of the proposed scheme are also provided.