With the growth of cloud computing infrastructure, its cost-efficient paradigm is driving a growing wave of small and medium-sized enterprises to migrate data and services to cloud based platforms. However, due to privacy concerns, data encryption prior to outsourcing is an important means of protection, which in turn requires performing queries over the encrypted data. While several approaches do offer support for privacy preserving skyline or top-k queries, they typically struggle with efficiency when extended to secure top-k dominating queries, due to the inherent nature of combining the advantages of both top k and skyline queries. Nevertheless, this nature renders them as a more practical and promising alternative for location-based services. To address this, we introduce STLD, a secure top-k location-based dominating query scheme. Specifically, we develop an innovative index structure called Secure Aggregate R-tree (SAR-tree) by utilizing the Paillier cryptosystem and introducing meticulously crafted noise, while also incorporating principles from aggregate R-trees and semi-blind R-trees. Leveraging this structure, we propose a series of secure sub-protocols to facilitate top-k dominating queries, accompanied by optimization techniques to mitigate latency associated with computationally intensive dominating operations. Given an encrypted query, STLD not only efficiently answers the query but also guarantees the privacy of data(sets), results, queries and access patterns. Finally, STLD undergoes rigorous theoretical security and complexity analysis, complemented by empirical evaluations that demonstrate its performance and feasibility, achieving a reduction in query cost by 40%-60% compared to multiple competing methods.

With the growing reliance on cloud services for large-scale data management, preserving the security and privacy of outsourced datasets has become increasingly critical. While encrypting data and queries can prevent direct content exposure, recent research reveals that adversaries can still infer sensitive information via access pattern and search path analysis. However, existing solutions that offer strong access pattern privacy often incur substantial performance overhead. In this paper, we propose a novel privacy-preserving range query scheme over encrypted datasets, offering strong security guarantees while maintaining high efficiency. To achieve this, we develop secure l earned spatial index (SLS-INDEX), a secure learned index that integrates the Paillier cryptosystem with a hierarchical prediction architecture and noise-injected buckets, enabling data-aware query acceleration in the encrypted domain. To further obfuscate query execution paths, SLS-INDEX- based Range Queries (SLRQ) employs a permutation-based secure bucket prediction protocol. Additionally, we introduce a secure point extraction protocol that generates candidate results to reduce the overhead of secure computation. We provide formal security analysis under realistic leakage functions and implement a prototype to evaluate its practical performance. Extensive experiments on both real-world and synthetic datasets demonstrate that SLRQ significantly outperforms existing solutions in query efficiency while ensuring dataset, query, result, and access pattern privacy.

Deep learning (DL) has been adopted in a broad range of Internet-of-Things (IoT) applications such as auto-driving, intelligent healthcare and smart grids, but limitations such as those relating to data and user privacy can complicate its broader implementation. Seeking to jointly address both privacy and utility, in this paper we connect the layer-wise relevance propagation with gradient descent for injecting proper noise into gradients. We also improve the conventional gradient clipping method by dividing the gradients into several groups; thus, minimizing the gradient distortion. Since the noisy gradient causes the undetermined descent direction and might adversely affect the loss minimization, we use the NoisyMin algorithm to select the best step size for each gradient perturbation. Finally, we integrate the adaptive optimizer into the gradient descent. In addition to improving the model utility, we also leverage the leading Sinh-Normal noise addition mechanism to achieve truncated concentrated differential privacy (tCDP) – as demonstrated by our rigorous analysis. Our experimental evaluations also validate the effectiveness of the proposed algorithm.

In a broad range of commercial and government applications, supporting secure location-based query services over outsourced cloud-based services particularly for data update on encrypted datasets remains challenging in practice. Compounding the challenge is the need to ensure update and query efficiency, dataset confidentiality (including against potentially malicious cloud service providers) and query authenticity. Thus in this paper, we propose DynPilot, a novel solution for privacy-preserving verifiable location-based skyline queries over dynamic and encrypted data(sets). The key challenge is how to devise a ciphertext-based authenticated data structure (ADS) that not only protects the confidentiality of the dataset (including the verification phase), but also the effective maintenance of such a dataset. Moreover, to motivate the cloud into actively updating ADS, the digest of the raw dataset is stored in the blockchain due to its immutability and consensus mechanism where update cost is also considered. Therefore, we present a novel ADS (hereafter referred to asDynamic-EfficientSecure andVerifiableTree (DSV-tree)), designed to be dynamic and support secure and verifiable skyline queries. Meanwhile, DynPilot also achieves forward privacy using a novel fuzzy update strategy. To further improve the efficiency of queries, an optimized version (i.e., DSV*-tree) is also developed based on the idea of the multi-level index structure. Finally, we analyze the security and complexity of our approach, and the empirical evaluations demonstrate the utility of our approach.

Supporting efficient and secure location-based skyline queries on encrypted data, such as private data outsourced to cloud-based systems, remains an ongoing challenge for efficiency due to significant computational costs in the ciphertext domain. To accelerate privacy-preserving skyline queries, the secure index intuitively contributes to an increase in efficiency. However, designing such a secure index is a challenge while protecting the unlinkability of queries. Meanwhile, there exist little work that can commendably assure efficiency and security. In this paper, we demonstrate SecSky, an efficient solution for supporting secure location-based skyline queries through the secure index. To support SecSky, we devise a novel unified structure, named secure R-tree (SR-tree) index, without privacy leakage (especially indirect privacy). Subsequently, we propose a novel secure location-based dominance protocol, which is utilized to calculate the dominance relationship on the SR-tree. Using this protocol as the building block, our secure location-based skyline query protocol integrates SR-tree, permutation and perturbation techniques to facilitate query processing so as to dramatically reduce the computational overhead. Meanwhile, our proposed solution avoids compromising the privacy of datasets, queries, dominance relationship and skyline results. Finally, we analyze the complexity and security of SecSky. Findings from the experimental evaluation show that our proposed scheme outperforms several other protocols by at least 3 orders of magnitude in terms of query efficiency.

Supporting secure location-based services on encrypted data that is outsourced to cloud computing platforms remains an ongoing challenge for efficiency due to expensive ciphertext calculation overhead. Furthermore, since the clouds may not be trustworthy or even malicious, data security and result authenticity has caused huge concerns. Unfortunately, little work can enable query efficiency, dataset confidentiality and result authenticity to be commendably guaranteed. In this paper, we demonstrate the potential of supporting secure and verifiable location-based skyline queries (SVLSQ). First, we devise a novel and unified structure, named semi-blind R-tree (SR-tree), which protects the query unlinkability. Based on SR-tree, we propose an authenticated data structure, named secure and verifiable scope R-tree (SVSR-tree). Then, we develop several secure protocols based on SVSR-tree to accelerate the query efficiency and reduce the size of verification objects. Our method avoids compromising the privacy of datasets, queries, results and access patterns. Meanwhile, it authenticates the soundness and completeness of the skyline results while preserving privacy. Finally, we analyze the complexity and security of SVLSQ. Findings from the performance evaluation illustrate that SVLSQ is a dramatically efficient method in terms of query (no less than 3 orders of magnitude faster than other solutions) and verification.

One existing challenge associated with large scale skyline queries on cloud services, particularly when dealing with private information such as biomedical data, is supporting multi-party queries with curious-but-honest parties on encrypted data. In addition, existing solutions designed for performing secure skyline queries incur significant communication and computation costs due to ciphertext calculation. Thus, in this paper, we demonstrate the potential of supporting privacy-preserving multi-party skyline queries on encrypted data using additive homomorphic and proxy re-encryption cryptosystems. However, the secure computation based on these cryptosystems will further slow down query efficiency. To improve the efficiency of comparison on encrypted data, we redesign two lightweight secure comparison protocols. Meanwhile, we present an efficient method named “blind-reading” to securely obtain the skyline point. We also propose a novel method, Privacy Matrix, designed to reduce the scale of the dataset so that the computational cost is significantly decreased without privacy leakage. Then, we construct our secure skyline query protocol by integrating lightweight secure comparison protocols, “blind-reading” and Privacy Matrix techniques. Finally, we evaluate the security of our protocol, where we show it is secure without leaking information. The performance evaluation also shows that our proposed approach significantly improves the efficiency (at least ×4.5 faster) compared to the-state-of-art and has the scalability of query processing under large datasets.

Dynamic wireless sensor networks (DWSNs) as an important means of industrial data collection are a key part of industrial Internet of Things (IIoT), where security and reliability are important characteristics of trustworthiness. However, due to dynamics, the security of key management is caused by a nontrusted base station (BS) that is easily targeted. For the distribution key management scheme, the avianized BS also causes additional and heavy overhead on sensors. To tackle these issues, in this article, we propose a blockchain-based secure key management scheme (BC-EKM). First, stake blockchain is constructed based on the hybrid sensor network. In addition, we design a secure cluster formation algorithm and a secure node movement algorithm to implement key management, where stake blockchain as a trust machine replaces the majority functions of the BS. Finally, we conduct the security analysis and ample simulations. The results indicate that that the BC-EKM scheme is effective and efficient, and better suited to improve the trustworthiness of DWSNs in the IIoT.