In real-world scenarios, computing the shortest path between given source and destination is widely prevalent, such as seeking the optimal route in a road network for navigation. However, in traditional non-private solutions, the user discloses its location information to the server in order to obtain the targeted shortest path, giving rise to a significant privacy leakage problem. Existing private shortest path computation schemes either provide limited privacy guarantees or require multiple interactions between the user and the server. In this paper, we introduce a fully private shortest path computation scheme, named Srchpa. This scheme ensures full privacy for both the location information provided by the user and the routing information held by the server. Furthermore, we propose a locally iterative computation method, achieving single-round interaction between the user and the server to obtain the targeted shortest path. Finally, we present the formal security analyses and comprehensive experiment evaluations. The security analyses demonstrate that our scheme achieves full privacy even if the server is malicious. The experiment evaluation results show that our scheme has lower computation, communication and storage costs on the user side, thus is practical for the lightweight user with limited resources.

Relational Triple Extraction (RTE) focuses on extracting triples from sentences, a crucial task in the automatic construction of knowledge graphs. Large Language Models (LLMs) have the ability to automatically extract triples from text through appropriate instructions or fine-tuning. However, due to the bias between LLMs training data and inference data, the previous LLM-based triple extraction method ignores many potentially valuable knowledge and lacks noise filtering, which greatly limits the capability of RTE model. To address these challenges, we propose Candidate Prompt Enhancement and Noise Filtering Relational Triple Extraction Framework Based on Large Language Models (CNRel), which combines small pre-trained language model and LLMs. Specifically, we first utilize a candidate entity pair extraction and filtering block, based on a small pre-trained language model, to extract and refine all possible entity pairs in the text, ensuring the capture of as much valuable information as possible Then, a fine-tuned LLMs such as LLaMA is then used to predict the relationship between the candidate entity pairs and extract as many triples as possible. Finally, Noise Filter block filter the extracted triples through LLMs, and remove the wrong triples, which greatly improve the precision of the RTE model. Experiments on several public datasets show that CNRel achieves state-of-the-art among all previous mainstream relational triple extraction methods, and we conduct a widely ablation experiments to reveal the contribution of each component to the overall performance.

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEIs) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding (RDH) with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the Chinese remainder theorem for polynomials (CRTPs). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach usingxorpreservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, number of pixels change rate and unified average changing intensity. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bits per pixel (bpp) and 2.0656 bpp, respectively.

In the realm of secure data outsourcing, verifiable dynamic searchable symmetric encryption (VDSSE) enables a client to verify search results obtained from an untrusted server while protecting the data privacy. Nevertheless, the storage cost of verification structure in some schemes escalates linearly with the number of keywords, and the generation of proofs demands a substantial number of exponentiation operations. Moreover, some schemes overlook forward and backward security in the dynamic database. In this article, we introduce FB-VDSSE, an advanced VDSSE scheme that ensures both forward and backward security. Specifically, we introduce an efficient accumulation commitment verification structure (AC-VS) that attains a commitment verification value with a constant-size storage cost. Based on the AC-VS, we further propose a forward and backward secure VDSSE scheme. Within this scheme, the server exclusively generates a membership proof at the corresponding index of the vector, reducing the computation cost associated with the search operation. Finally, we provide the security proof and functional comparison, demonstrating that our scheme effectively ensures forward security, backward security, and verifiability. Additionally, the experimental evaluations underscore the efficiency of our scheme, showcasing its superior performance compared to relevant schemes in practical scenarios.

Similarity search on encrypted data can identify similar data and handle misspelled keywords in a privacy-preserving manner and thus has received a lot of attention. However, existing schemes suffer from imprecise or predefined distance thresholds, which means that they do not always return the expected search results. Moreover, these schemes either do not consider document addition or lack forward security in this dynamic setting. In this article, we present a Similar Keyword Matching (SKM) framework that accurately calculates the Hamming distance between keywords through a new keyword representation called uni-pos-gram. Based on our framework, we propose a basic scheme for similarity search over encrypted data called SimSE that offers adjustable Hamming distance thresholds and an enhanced scheme called SimSE-F that provides forward security. Security analysis demonstrates that our schemes effectively safeguard the privacy of documents, indexes, and searches. Empirical experiments using real-world datasets demonstrate the efficiency and practical applicability of our schemes.

With the rapid growth of cloud storage technology, the demand for efficient and secure search of outsourced encrypted data has become increasingly critical. However, existing conjunctive keyword dynamic searchable encryption schemes often expose the Keyword Pair Result Pattern (KPRP) during index matching, compromising privacy. Additionally, frequent index updates require expensive group exponentiations, leading to high client-side overhead. To tackle these challenges, we propose LRP-HDSE, a lightweight dynamic conjunctive keyword searchable encryption scheme that hides KPRP while minimizing client computation costs. To enhance privacy, we introduce the Vector Hidden Subset Predicate Encryption (VH-SPE) mechanism, which enables the server to implicitly detect cross-tag in the membership matching index, effectively mitigating KPRP leakage. For improved efficiency, the scheme designs a lightweight membership matching index structure, LSet, based on low-cost multiset hash operations, reducing reliance on costly exponentiations and lowering client overhead. Our security analysis confirms that LRP-HDSE provides robust KPRP hiding along with forward and backward security in dynamic environments. Asymptotic analysis, along with experiment evaluations on two real-world datasets, show that our scheme offers superior client-side computational efficiency compared to existing approaches, making it both practical and effective.

The continuous development of mobile terminal applications, online maps, and other navigation services have become widely used, simultaneously giving rise to significant security risks. To address the issues of privacy leakage and low efficiency in traditional graph shortest path retrieval schemes, an efficient privacy-preserving graph shortest path retrieval scheme is proposed, called EP-GSPR. Specifically, this scheme addresses the privacy security problems in the existing graph shortest path retrieval solutions by ensuring the bilateral privacy protection of the user's query location and the database privacy of the cloud server. Throughout the retrieval process, the cloud server cannot obtain the user's location information, and the user cannot access any database information other than the retrieval results. To overcome the performance bottlenecks in existing schemes, a progressive iterative retrieval framework is designed as the fundamental modular, called Pirf, achieving sub-linear retrieval costs and low storage overhead on the cloud server side. Finally, the security analyses demonstrate the EP-GSPR scheme achieves the bilateral privacy-preserving in terms of user and server sides. The comprehensive experiment evaluations also state the efficiency and practicality of the proposed scheme

As data sharing becomes more prevalent, there is growing interest in multiuser dynamic searchable symmetric encryption (MU-DSSE). It enables multiple authorized users to search the encrypted database while safeguarding data privacy. However, most existing schemes are inefficient due to complex computation operations and unaffordable storage burdens. In addition, some are susceptible to collusion attacks between cloud server and compromised users, leading to the leakage of search privacy from other legitimate users. To overcome these challenges, we propose a practical MU-DSSE scheme featuring an unlinkable key derivation mechanism to thwart collusion attacks. Moreover, the MU-DSSE scheme ensures both forward and backward securities in the dynamic setting. To enhance efficiency, we introduce an innovative identity-based key encapsulation mechanism for distributing authorization information to multiple users, significantly optimizing computation and storage costs on user sides and the data owner. Furthermore, we provide the formal security proof and performance analyses. The experimental results demonstrate that MU-DSSE incurs the constant-size storage cost on user sides and the data owner, and outperforms the existing schemes in practice.