Privacy-preserving Multi-keyword Ranked Search over Encrypted Cloud Data Supporting Dynamic Update

Xingming Sun,Lu Zhou,Zhangjie Fu,Jin Wang 보안공학연구지원센터 2014 International Journal of Security and Its Applicat Vol.8 No.6

With the development of cloud computing, the sensitive information of outsourced data is at risk of unauthorized accesses. 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. Hence, it is an especially important thing to explore secure encrypted cloud data search service. Considering the huge number of outsourced data, there are three problems we are focused on to enable efficient search service: multi-keyword search, result relevance ranking and dynamic update. In this paper, we propose a practically efficient and flexible searchable encrypted scheme which supports both multi-keyword ranked search and dynamic update. To support multi-keyword search and result relevance ranking, we adopt Vector Space Model (VSM) to build the searchable index to achieve accurate search result. To improve search efficiency, we design a tree-based index structure which supports insertion and deletion update well without privacy leakage. We propose a secure search scheme to meet the privacy requirements in the threat model. Finally, experiments on real-world dataset are implemented to demonstrate the overall performance of the proposed scheme, which show our scheme is efficient.

SLQE : An Improved Link Quality Estimation based on Four-bit LQE

An Zhou,Baowei Wang,Xingming Sun,Xingang You,Huiyu Sun,Tao Li 보안공학연구지원센터 2015 International Journal of Future Generation Communi Vol.8 No.1

Link quality estimation (LQE) is an effective basic building block in wireless sensor networks (WSNs) and higher cross layer design of network protocol. Some researchers have investigated the statistical properties of the link quality estimators independently from higher-layer protocols, and their impact on the Collection Tree Routing Protocol (CTP). Then they set up a dedicated LQE, independent of the protocol interface, which has in total of four bits information: one from the physical layer, one from the link layer, and two from the network layer. Four-bit has been found to be a good estimator; however its performance heavily depends on the tuning of its parameters. But we found that Four-bit couldn’t be working effectively in responding to the burst situation after repeated experiments. So we redesigned the link estimation method, called Stable Link Quality Estimation (SLQE), which combines active probing with passive snooping to make estimation more stable. We have found that the new design can cope with the emergency. Moreover it also enhances the robustness of the network, and saves the overall energy consumption of the network.

Efficient Keyword Search Scheme in Encrypted Cloud Computing Environment

Jiangang Shu,Xingming Sun,Lu Zhou,Jin Wang 보안공학연구지원센터 2014 International Journal of Grid and Distributed Comp Vol.7 No.5

With the increasing popularity of cloud computing, more and more sensitive or private information has been outsourced onto the cloud server. For protecting data privacy, sensitive data usually has to be encrypted before outsourcing, which makes traditional search techniques based on plaintext useless. In response to the search over encrypted data, searchable encryption is a good solution in Information Security. However, most of existing searchable encryption schemes only support exact keyword search. That means they don’t support searching for different variants of the query word, which is a significant drawback and greatly affects data usability and user experience. Recently, a fuzzy keyword search scheme proposed by some researchers aims at addressing the problems of minor typos and format inconsistence but couldn’t solve the problem above. In this paper, we formalize the problem of semantic keyword-based search over encrypted cloud data while preserving privacy. Semantic keyword-based search will greatly improves the user experience by returning all the documents containing semantically close keywords related to the query word. In our solution, we use the stemming algorithm to construct stem set, which reduces the dimension of index. And the symbol-based trie is also adopted in index construction to improve the search efficiency. Through rigorous privacy analysis and experiment on real dataset, our scheme is secure and efficient.

Verifiable Text Watermarking Detection to Improve Security

Zhangjie Fu,Xingming Sun,Jiangang Shu,Lu Zhou,Jin Wang 보안공학연구지원센터 2014 International Journal of Security and Its Applicat Vol.8 No.5

Evolving Recommender System for Mobile Apps

Xiao Xia,Xiaodong Wang,Xingming Zhou 한국산학기술학회 2013 SmartCR Vol.3 No.3

The explosive growth of mobile apps gives rise to the significant challenge of app discovery. For this reason, Google Play utilizes a collaborative filtering approach for recommending apps to users by analyzing user behavior. Those recommendations help users discover apps by referring to the experiences of other users. However, their choices may also be limited because most users only know about a limited number of apps. To eliminate such constraints, we propose a novel recommendation method utilizing global information about apps. We generate recommendations by both analyzing the metadata and measuring the similarity between apps, leveraging the Latent Semantic Index method. To understand both methods, we further measure the diversity within them. Through those measurements, we not only gain better understanding of both recommendation methods but also discover new knowledge about user preferences. Such measurements also identify the necessity and potential to evolve the existing system. We therefore propose a diversity measurement?based evolution framework for the development of mobile app recommender systems. To implement the framework, we further model the system evolution as a multi-criteria optimization problem and design a rank aggregation scheme to solve it. Preliminary evaluations verified the promising effectiveness of our framework and method based on a data set of 103,348 apps.

Reversible Watermarking with Adaptive Embedding Threshold Matrix

( Guangyong Gao ),( Yun-qing Shi ),( Xingming Sun ),( Caixue Zhou ),( Zongmin Cui ),( Liya Xu ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.9

In this paper, a new reversible watermarking algorithm with adaptive embedding threshold matrix is proposed. Firstly, to avoid the overflow and underflow, two flexible thresholds, TL and TR, are applied to preprocess the image histogram with least histogram shift cost. Secondly, for achieving an optimal or near optimal tradeoff between the embedding capacity and imperceptibility, the embedding threshold matrix, composed of the embedding thresholds of all blocks, is determined adaptively by the combination between the composite chaos and the average energy of Integer Wavelet Transform (IWT) block. As a non-liner system with good randomness, the composite chaos is suitable to search the optimal embedding thresholds. Meanwhile, the average energy of IWT block is calculated to adjust the block embedding capacity, and more data are embedded into those IWT blocks with larger average energy. The experimental results demonstrate that compared with the state-of-the-art reversible watermarking schemes, the proposed scheme has better performance for the tradeoff between the embedding capacity and imperceptibility.

Intention-Oriented Itinerary Recommendation Through Bridging Physical Trajectories and Online Social Networks

( Xiangxu Meng ),( Xinye Lin ),( Xiaodong Wang ),( Xingming Zhou ) 한국인터넷정보학회 2012 KSII Transactions on Internet and Information Syst Vol.6 No.12

Compared with traditional itinerary planning, intention-oriented itinerary recommendations can provide more flexible activity planning without requiring the user`s predetermined destinations and is especially helpful for those in unfamiliar environments. The rank and classification of points of interest (POI) from location-based social networks (LBSN) are used to indicate different user intentions. The mining of vehicles` physical trajectories can provide exact civil traffic information for path planning. This paper proposes a POI category-based itinerary recommendation framework combining physical trajectories with LBSN. Specifically, a Voronoi graph-based GPS trajectory analysis method is utilized to build traffic information networks, and an ant colony algorithm for multi-object optimization is implemented to locate the most appropriate itineraries. We conduct experiments on datasets from the Foursquare and GeoLife projects. A test of users` satisfaction with the recommended items is also performed. Our results show that the satisfaction level reaches an average of 80%.

Distributed Collision-Resolvable Medium Access Control for Wireless LANs with Interference Cancellation Support

( Hu Shen ),( Shaohe Lv ),( Xiaodong Wang ),( Xingming Zhou ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.8

Medium access control is critical in wireless networks for efficient spectrum utilization. In this paper, we introduce a novel collision resolution method based on the technique of known interference cancellation, and propose a new MAC protocol named as CR-MAC, in which AP tries to decode all the collided data packets by combining partial retransmissions and known interference cancellation. As the collided transmissions are fully utilized, less retransmission is required, especially in a crowded network. The NS-2simulation and MATLAB numerical results show that, under various network settings, CR-MAC performs much better than the IEEE 802.11 DCF in terms of the aggregation throughput and the expected packet delay.

Energy-efficient Routing in MIMO-based Mobile Ad hoc Networks with Multiplexing and Diversity Gains

( Hu Shen ),( Shaohe Lv ),( Xiaodong Wang ),( Xingming Zhou ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.2

It is critical to design energy-efficient routing protocols for battery-limited mobile ad hoc networks, especially in which the energy-consuming MIMO techniques are employed. However, there are several challenges in such a design: first, it is difficult to characterize the energy consumption of a MIMO-based link; second, without a careful design, the broadcasted RREP packets, which are used in most energy-efficient routing protocols, could flood over the networks, and the destination node cannot decide when to reply the communication request; third, due to node mobility and persistent channel degradation, the selected route paths would break down frequently and hence the protocol overhead is increased further. To address these issues, in this paper, a novel Greedy Energy-Efficient Routing (GEER) protocol is proposed: (a) a generalized energy consumption model for the MIMO-based link, considering the trade-off between multiplexing and diversity gains, is derived to minimize link energy consumption and obtain the optimal transmit model; (b) a simple greedy route discovery algorithm and a novel adaptive reply strategy are adopted to speed up path setup with a reduced establishment overhead; (c) a lightweight route maintenance mechanism is introduced to adaptively rebuild the broken links. Extensive simulation results show that, in comparison with the conventional solutions, the proposed GEER protocol can significantly reduce the energy consumption by up to 68.74%.

A Genetic Approach for Joint Link Scheduling and Power Control in SIC-enable Wireless Networks

( Xiaodong Wang ),( Hu Shen ),( Shaohe Lv ),( Xingming Zhou ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.4

Successive interference cancellation (SIC) is an effective means of multi-packet reception to combat interference at the physical layer. We investigate the joint optimization issue of channel access and power control for capacity maximization in SIC-enabled wireless networks. We propose a new interference model to characterize the sequential detection nature of SIC. Afterward, we formulize the joint optimization problem, prove it to be a nondeterministic polynomial-time-hard problem, and propose a novel approximation approach based on the genetic algorithm (GA). Finally, we discuss the design and parameter setting of the GA approach and validate its performance through extensive simulations.