ng He

Secure, Efficient and Fine-Grained Data Access Control Mechanism for P2P Storage Cloud

By combining cloud computing and Peer-to-Peer computing, a P2P storage cloud can be formed to offer highly available storage services, lowering the economic cost by exploiting the storage space of participating users. However, since cloud severs and users are usually outside the trusted domain of data owners, P2P storage cloud brings forth new challenges for data security and access control when data owners store sensitive data for sharing in the trusted domain. Moreover, there are no mechanisms for access control in P2P storage cloud. To address this issue, we design a ciphertext-policy attribute-based encryption (ABE) scheme and a proxy re-encryption scheme. Based on them, we further propose a secure, efficient and fine-grained data Access Control mechanism for P2P storage Cloud named ACPC. We enforce access policies based on user attributes, and integrate P2P reputation system in ACPC. ACPC enables data owners to delegate most of the laborious user revocation tasks to cloud servers and reputable system peers. Our security analysis demonstrates that ACPC is provably secure. The performance evaluation shows that ACPC is highly efficient under practical settings, and it significantly reduces the computation overheads brought to data owners and cloud servers during user revocation, compared with other state-of-the-art revocable ABE schemes.

An Efficient SSD-based Hybrid Storage Architecture for Large-Scale Search Engines

Large-scale search engines use hard disk drives (HDD) to store the mass index data for their capacity, whose performances are limited by the relatively low I/O performance of HDD. Caching is an effective optimization, and many caching algorithms have been proposed to improve retrieval performance. Considering the high cost of memory and huge amounts of data, the limited capacity of cache in memory cannot resolve the above problem thoroughly. In this paper, we adopt a solid state disk (SSD) based storage architecture, which uses SSD as a secondary cache for memory. We analyze the I/O patterns of search engines and propose SSD-based data management policies based on the hybrid storage architecture, including data selection, data placement and data replacement. Our main goal is to improve the performance of search engines while reducing operation cost inside SSD. The experimental results demonstrate the proposed architecture improves the hit ratio by 13.31%, the performance by 41.05%, the average access time inside SSD by 43.83%, and reduces block erasure operations by 71.52%.

An Integrated System Solution for Secure P2P Content Distribution Based on Network Coding

Network coding has been demonstrated to be able to improve the performance of P2P content distribution. However, it is vulnerable to pollution attacks, leading to substantial performance degradation. Moreover, existing corruption detection schemes for network coding are not applied well to P2P systems. More efficient scheme based on attacker identification is required to thwart such attacks. In this paper, we propose an integrated system solution for secure P2P content distribution based on network coding, referred to as ISNC. In ISNC, we first design our system architecture based on extended uniform bipartite networks that can achieve high throughput with network coding. Based on the architecture, we present a secure network coding signature scheme and an identity-based malicious peer identification scheme. The two schemes can cooperate to thwart pollution attacks effectively in P2P network, not only detecting corrupted blocks, but also identifying all the malicious peers. Simulation results show that ISNC can effectively limit the pollution spread and identify malicious peers quickly, even when they collude to launch attacks. Compared with existing related schemes, ISNC is especially applicable for P2P content distribution, and can achieve both high security and overall efficiency.

An efficient ECC-based mechanism for securing network coding-based P2P content distribution

Network coding has been demonstrated to be able to improve the performance of P2P content distribution. However, it is vulnerable to pollution attacks where malicious peers can flood the network with corrupted blocks easily, leading to substantial performance degradation. Moreover, existing corruption detection schemes for network coding are not well suited to P2P systems. Effective scheme to detect the corruption and identify the attacker is required to thwart such attacks. In this paper, we propose an efficient ECC-based mechanism for securing network coding-based P2P content distribution, namely ESNC, which includes an efficient network coding signature scheme and an identity-based malicious peer identification scheme. The two schemes cooperate to thwart pollution attacks on network coding effectively in P2P networks, not only detecting corrupted blocks on-the-fly efficiently, but also precisely identifying all the malicious peers quickly. ESNC is mainly based on elliptic curve cryptography (ECC) and can provide high level of security. It incurs significantly less computation and communication overheads than other comparable state-of-the-art schemes for P2P systems. ESNC can work with arbitrary topologies, as it is the case in P2P networks. Security analysis demonstrates that ESNC can resist hash collision attacks, signature forgery attacks, and collusion attacks with arbitrary number of colluding malicious peers. Simulation results show that ESNC effectively limits the corruption spread and identifies all the malicious peers in a short time under different practical settings.

Distributed caching in unstructured peer-to-peer file sharing networks

A novel distributed caching algorithms in unstructured peer-to-peer file sharing networks.A distance-based network infrastructure with preference-based three-way random walk.Probability-based and greedy-based caching placement and replacement algorithms.Compromising and balancing the resources allocated for objects with different popularity to remedy the over-caching problem. Nowadays, the peer-to-peer (P2P) system is one of the largest Internet bandwidth consumers. To relieve the burden on Internet backbone and improve the query and retrieve performance of P2P file sharing networks, efficient P2P caching algorithms are of great importance. In this paper, we propose a distributed topology-aware unstructured P2P file caching infrastructure and design novel placement and replacement algorithms to achieve optimal performance. In our system, for each file, an adequate number of copies are generated and disseminated at topologically distant locations. Unlike general believes, our caching decisions are in favor of less popular files. Combined with the underlying topology-aware infrastructure, our strategy retains excellent performance for popular objects while greatly improves the caching performance for less popular files. Overall, our solution can reduce P2P traffic on Internet backbone, and relieve the over-caching problem that has not been properly addressed in unstructured P2P networks. We carry out simulation experiments to compare our approaches with several traditional caching strategies. The results show that our algorithms can achieve better query hit rates, smaller query delay, higher cache hit rates, and lower communication overhead.

Fast Snippet Generation Based on CPU-GPU Hybrid System

As an important part of searching result presentation, query-biased document snippet generation has become a popular method of search engines that makes the result list more informative to users. Generating a single snippet is a lightweight task. However, it will be a heavy workload to generate multiple snippets of multiple documents as the search engines need to process large amount of queries per second, and each result list usually contains several snippets. To deal with this heavy workload, we propose a new high-performance snippet generation approach based on CPU-GPU hybrid system. Our main contribution of this paper is to present a parallel processing stream for large-scale snippet generation tasks using GPU. We adopt a sliding document segmentation method in our approach which costs more computing resources but can avoid the common defect that the high relevant fragment may be cut off. The experimental results show that our approach gains a speedup of nearly 6 times in average process time compared with the baseline approach-Highlighter.

EDS: An Efficient Data Selection policy for search engine storage architectures

Abstract Caching is an effective optimization in search engine storage architectures. Many caching algorithms have been proposed to improve retrieval performance. The data selection policy of search engine cache management plays an important role, which carefully places the data in memory or other storage, such as solid state disks (SSDs). Considering that the historical query log has a guiding role for the future query, we present an Efficient Data Selection (EDS) policy for search engine cache management, which views cache media as a knapsack, and views results and posting lists as items. The best benefit of EDS can be computed by greedy algorithms. We carry out a series of experiments to study the essential factors of the data selection in different architectures, including hard disk drive (HDD), SSD, and SSD-based hybrid storage architectures. The hybrid storage architecture is a two-level cache architecture, which uses SSD as a secondary cache for the memory. Our main goal is to improve the performance of the search engines and reduce the cost of the servers on two-level cache architecture. The experimental results demonstrate that our proposed policy improves the hit ratio by 20.04% as well as the retrieval performance on HDD, SSD, and hybrid architecture by 31.98%, 28.72% and 23.24%, respectively.

A Lightweight Secure Data Sharing Scheme for Mobile Cloud Computing

With the popularity of cloud computing, mobile devices can store/retrieve personal data from anywhere at any time. Consequently, the data security problem in mobile cloud becomes more and more severe and prevents further development of mobile cloud. There are substantial studies that have been conducted to improve the cloud security. However, most of them are not applicable for mobile cloud since mobile devices only have limited computing resources and power. Solutions with low computational overhead are in great need for mobile cloud applications. In this paper, we propose a lightweight data sharing scheme (LDSS) for mobile cloud computing. It adopts CP-ABE, an access control technology used in normal cloud environment, but changes the structure of access control tree to make it suitable for mobile cloud environments. LDSS moves a large portion of the computational intensive access control tree transformation in CP-ABE from mobile devices to external proxy servers. Furthermore, to reduce the user revocation cost, it introduces attribute description fields to implement lazy-revocation, which is a thorny issue in program based CP-ABE systems. The experimental results show that LDSS can effectively reduce the overhead on the mobile device side when users are sharing data in mobile cloud environments.

An Efficient Data Selection Policy for Search Engine Cache Management

Caching is an effective optimization in search engine. The data selection policy plays a key role in caching, which places the data to be cached in memory. However, the current data selection policies are not suitable to the hybrid storage architecture with solid state disks (SSDs), which have gradually replaced hard disk drives (HDDs) in search engines. In this paper, we present an Efficient Data Selection policy (EDS) for search engine cache management, which views cache media as a knapsack, and views results and posting lists as items. The best benefit can be computed by greedy algorithms. In order to verify the effectiveness, we carry out a series of experiments to study essential factors of data selection in different architectures, including HDD, SSD, and SSD-based hybrid storage architecture, which uses SSD as a secondary cache for memory. The experimental results demonstrate that the proposed policy improves the hit ratio by 20.04% and the retrieval performance on HDD, SSD, and hybrid architecture by 31.98%, 28.72% and 23.24%, respectively.