Cloud computing is becoming increasingly popular. A large number of data are outsourced to the cloud by data owners motivated to access the large-scale computing resources and economic savings. To protect data privacy, the sensitive data should be encrypted by the data owner before outsourcing, which makes the traditional and efficient plaintext keyword search technique useless. So how to design an efficient, in the two aspects of accuracy and efficiency, searchable encryption scheme over encrypted cloud data is a very challenging task. In this paper, for the first time, we propose a practical, efficient, and flexible searchable encryption scheme which supports both multi-keyword ranked search and parallel search. To support multi-keyword search and result relevance ranking, we adopt Vector Space Model (VSM) to build the searchable index to achieve accurate search results. To improve search efficiency, we design a tree-based index structure which supports parallel search to take advantage of the powerful computing capacity and resources of the cloud server. With our designed parallel search algorithm, the search efficiency is well improved. We propose two secure searchable encryption schemes to meet different privacy requirements in two threat models. Extensive experiments on the real-world dataset validate our analysis and show that our proposed solution is very efficient and effective in supporting multi-keyword ranked parallel searches.

This paper presents a content-addressable memory (CAM) using a phase-change device. A hierarchical match-line structure and a one-hot-spot block code are indispensable to suppress the resistance ratio of the phase-change device and the area overhead of match detectors. As a result, an 8-nsec 72-bit-parallel-search CAM is implemented using a phase-change-device/MOS-hybrid circuitry, where high and low resistances are higher than 2.3 MΩ and lower than 97 kΩ, respectively, while maintaining one-day retention.

The realization of k-nearest-matches search capability in fully-parallel mixed digital-analog associative memories by a sequential autonomous search mode is reported. The proposed concept and circuit implementation can be applied with all types of distance measures such as Hamming, Manhattan or Euclidean distance search, and the k value can be freely selected during operation. A test chip for concept verification has been designed in 0.35 µm CMOS technology with two-poly, three-metal layers, realizes k-nearest-matches Euclidean distance search and consumes 5.12 mm2 of the chip area for 64 reference patterns each with 16 units of 5-bit.

Overheads caused by frequently communicating α-β values among numerous parallel search processes not only degrade greatly the performance of existing parallel α-β search algorithm but also make it impractical to implement these algorithms in parallel hardware. To solve this problem, the proposed architecture reduces the overheads by using specially designed multi-value arbiters to compare and global broadcasting buses to communicate α-β values. In addition, the architecture employs a set of α-β search control units (α-β SCU's) with distributed α-β registers to accelerate the search by searching all subtrees in parallel. Simulation results show that the proposed parallel architecture with 1444 (38 38) (α-β SCU's) searching in parallel can achieve 179 folds of speed-up. To verify the parallel architecture, we implemented a VLSI chip with 3 α-β SCU's. The chip can achieve a search speed of 13,381,345 node-visits per second, which is more than three orders of improvement over that of existing parallel algorithms.