Data compression techniques significantly improve storage efficiency, bandwidth utilization, and energy efficiency, yet they introduce challenges for the rapid browsing and retrieval of valuable information within compressed data. Existing approaches achieve high-speed, lossless matching by exploiting the context-free property of automata. However, they are constrained by the recursive reference structures in compressed data, which necessitate state copying to ensure matching safety.

Blockchain technology is a decentralized distributed database technology, which greatly improves the security and credibility of the data exchange process through decentralization. A great deal of research has already been conducted on combining blockchain and vehicular ad-hoc networks(VANETs) applications to solve the problems of opaque user transactions, data tampering, and insufficient motivation of participating parties. However, in the large-scale VANETs, the commonly used blockchain consensus mechanism PBFT suffers from poor scalability, high communication volume, and insufficient control of node behaviour. Therefore, in this paper, an improved consensus mechanism N-PBFT is designed for the field of VANETs based on the construction of vehicle trust management system. The improved consensus mechanism N-PBFT solves the problems of node identity peering, poor scalability, and high communication volume. After experimental testing, the proposed reputation system is able to effectively manage the information of the VANETs. And the improved PBFT consensus algorithm has a significant performance improvement over the traditional PBFT, SG-PBFT and RIPPB in terms of both throughput and latency.

Blockchain has emerged as a prevailing paradigm for decentralized applications due to its reliability and transparency. To scale up retrieval services, a common strategy is to use a hybrid storage model, where on-chain storage is responsible for small metadata and off-chain storage is for outsourced raw data. However, data security and result authenticity are ongoing challenges in this scenario, and little work has been done due to the difficulty of combining result verification and privacy preservation, especially for dynamic updates while supporting forward privacy. In this paper, we formally define the problem of efficient, verifiable, and secure conjunctive keyword search in hybrid-storage blockchains (vsChain) and propose a novel hybrid index that achieves efficient query and verification while supporting dynamic updates with forward privacy guarantee. Finally, we provide empirical evaluations using real and synthetic datasets to demonstrate the feasibility of our proposed scheme.

Blockchain has emerged as a prevailing paradigm for decentralized applications due to its reliability and transparency. To scale up retrieval services, a common strategy is to use a hybrid storage model, where on-chain storage is responsible for small metadata and off-chain storage is for outsourced raw data. However, data security and result authenticity are ongoing challenges in this scenario, and little work has been done due to the difficulty of combining result verification and privacy preservation, especially for dynamic updates while supporting forward privacy. In this paper, we formally define the problem of efficient, verifiable, and secure conjunctive keyword search in hybrid-storage blockchains (vsChain) and propose a novel hybrid index that achieves efficient query and verification while supporting dynamic updates with forward privacy guarantee. We also design two optimized schemes to improve query and verification performance by using a partition-based method and an obfuscated counting Bloom filter mechanism. Finally, we provide a theoretical security analysis and empirical evaluations using real and synthetic datasets to demonstrate the feasibility of our proposed schemes.

With the boom in cloud computing, data outsourcing in location-based services is proliferating and has attracted increasing interest from research communities and commercial applications. Nevertheless, since the cloud server is probably both untrusted and malicious, concerns about data security and result integrity have become on the rise sharply. In addition, in the single-user situation assumed by most existing works, query users can capture query content from each other even though the queries are encrypted, which may incur the leakage of query privacy. Unfortunately, there exists little work that can commendably assure data security and result integrity in the multi-user setting. To this end, in this article, we study the problem of multi-user, secure, and verifiable$k$nearest neighbor query (MSV$k$kNN). To support MSV$k$NN, we first propose a novel unified structure, called verifiable and secure index (VSI). Based on this, we devise a series of secure protocols to facilitate query processing and develop a compact verification strategy. Given an MSV$k$NN query, our proposed solution can not merely answer the query efficiently while can guarantee: 1) preservingdata privacy,query privacy,result privacy, andaccess patterns privacy; 2) authenticating thecorrectnessandcompletenessof the results; 3) supportingmulti-userwith different keys. Finally, the formal security analysis and complexity analysis are theoretically proven and the performance and feasibility of our proposed approach are empirically evaluated and demonstrated.

With the explosive growth of short texts on users' preferences, learning discriminative and coherent latent topics from short texts is a critical work, since many practical applications require semantic understandings that short texts convey explicitly and implicitly. However, existing short text topic learning methods face the challenge of fully capturing semantically related co-occurrence phrases. Therefore, this paper proposes a novel Heterogeneous Information Network-based Short Text Topic learning approach (HIN-ShoTT) in terms of parts of speech, without depending on any auxiliary information. Specifically, HIN-ShoTT can be decomposed into three phases: i) seeking semantic relations among words, where HIN-ShoTT models multiple semantic relations among words based on a Heterogeneous Information Network (HIN) in terms of parts of speech; ii) extracting co-occurrence phrases and filtering noises, where HIN-ShoTT defines parts-of-speech meta structures to guide co-occurrence phrase extraction and a self-adapting threshold filtering module is proposed for discarding noises; and iii) inferring topics, where HIN-ShoTT models the generative process of co-occurrence phrases to make topic learning effective with the abundant corpus-level information. Our experimental results on three real-world datasets not only show that HIN-ShoTT performs well, but also demonstrate that it is feasible to incorporate HIN into short text topic learning for accuracy improvement.

For comprehensive analyzing or efficient searching from massive data, string matching is widely used as a core technique of the network traffic detection applications and text editors. However, the increasing compressed text challenges string matching to achieve high-speed processing. In this paper, we propose KCM, a fast Knuth-Morris-Pratt based string matching method over LZ77 compressed text. It leverages the gathered heuristic information during scanning to skip the characters that should have been scanned. In our evaluation with real traffic, KCM skips more than 90% compression text, which nearly approaches the theoretical upper bound. It can achieve 1.61 Gbps throughput and boost 1.87 times than the classic string matching.