To ensure the legitimacy of communicators while ad dressing the privacy concerns of vehicles in vehicular ad-hoc networks (VANETs), conditional privacy-preserving authentication (CPPA) schemes have been proposed. Given that existing schemes suffer from single point of failure due to centralized authorities, several distributed CPPA schemes have been proposed. However, these schemes all ignore the tight cementation between system secret keys and the authority, which could be a serious threat to system security, that the compromised authority may leak the system secret key. To address these issues, we propose a security enhanced decentralized conditional privacy-preserving authentication (DCPPA) scheme. DCPPA first introduces a decentralized system master key generation (DSMKG) mechanism without a centralized secret sharer, ensuring that the system secret key remains hidden from any single authority. Based on DSMKG, DCPPA then implements a lightweight verifiable pseudonym self-generation strategy without the system master secret key escrow problem, thus providing flexible pseudonym updating and reliable de-anonymization. Moreover, we implement DCPPA over a hyperelliptic curve cryptosystem (HECC) to balance the system performance. Considering the additional communication processes due to the decentralized feature, we introduce a symmetric balanced incomplete block design (SBIBD) to enhance the communication efficiency. We demonstrate the excellent security of DCPPA through an in-depth security analysis, while demonstrate that the overhead of DCPPA is at ms level through experiments.

Collaborative authentication, boasting-enhanced accuracy, robust resilience, and optimized efficiency, holds immense promise for autonomous aerial vehicle (AAV) networks. However, existing collaborative authentication methods overlook both the credibility evaluation and incentives of participating nodes, thereby compromising authentication accuracy and resulting in failures. Furthermore, reliance on trusted decision-fusion institution introduces vulnerabilities and single points of failure. To address these issues, we design a credibility-weighted soft authentication approach specifically for AAV networks, thereby enhancing accuracy by effectively integrating the trustworthiness of collaborating nodes. To further encourage active participation from nodes, we introduce an incentive-based reputation system. Finally, based on the above approaches, we propose a distributed authentication method by leveraging blockchain technology and optimization theory that not only emphasizes security but also optimizes resource selection in AAV networks. Theoretical analysis demonstrates our scheme’s distributed authentication with minimized resource consumption under specified security strength, mitigating single points of failure and fulfilling efficient mutual authentication requirements. Experimental results show a remarkable 78.45% increase in authentication accuracy and a 44.51% reduction in resource consumption compared to advanced solution.

In Unmanned Aerial Vehicle (UAV) networks, UAVs usually perform tasks in the form of groups. When tasks change, the Ground Control Station (GCS) will assign the complemental UAV to join the group for notification or reinforcement. Since UAVs communicate over open wireless channels, secure authentication is required for complemental UAV joining the group. However, one-by-one authentication between complemental UAV and the group members leads to high overhead and delays. At the same time, when the UAV group is far away from the coverage of the GCS, the GCS is unable to assist the authentication process in real-time. To solve the above problems, we propose a one-to-many UAV authentication scheme using Identity-Based Broadcast Encryption (IBBE) and batch authentication. This scheme does not require a trusted third party to be online in real time. We also design an algorithm based on reinforcement learning for identifying illegal requests during batch authentication, enhancing efficiency and ensuring successful authentication. Our scheme meets UAV networks’ security requirements, defending against various attacks. Experimental results show that it reduces computational overhead by 55.27% and communication overhead by 23.16% compared to similar schemes. Additionally, the illegal request identification algorithm reduces identification numbers by 15.44% to 25.72% and lowers latency by 14.64% to 25.12% compared to existing methods.

With the rapid expansion of data volumes in cloud computing, more data owners are opting to outsource their data to cloud service providers to reduce local storage and management costs. However, data outsourcing deprives data owners of direct physical control over their data, increasing the risk of unauthorized access and exposure of sensitive information. To mitigate these risks, various privacy-preserving keyword search schemes with access control have been developed, but many are vulnerable to leakage-abuse attacks due to the exposure of access, search or volume patterns, which can lead to privacy breaches in outsourced data and queries. To solve this problem, we propose an oblivious encrypted keyword search scheme with fine-grained access control, called OEKA. It enables efficient oblivious keyword search over encrypted multi-maps by using the adapted XOR filter and distributed point function, ensuring protection of access, search and volume patterns. Moreover, OEKA enforces role-based access control by using polynomial-based access strategy and keyword-based private information retrieval, allowing access policies of retrieved objects to be detecting without revealing the objects themselves. A formal security analysis verifies the scheme’s robustness, and experimental results demonstrate its practical efficiency.

As a fundamental service in mobile cloud computing, range query has attracted extensive attention. But the existing secure range query schemes not only leak data privacy but also have low query efficiency. To address those issues, we first design a novel range-matched code to convert the range query into code set matching, which aims to hide the order relationship of outsourced data as well as the index of most significant different bit. Based on the designed range-matched code, we propose aPractical andLess LeakageRangeQuery scheme over encrypted mobile cloud data (PLRQ) by integrating XOR filter and multiset hash function. Security analysis shows that PLRQ achieves semantic security and avoids data privacy leakage. Extensive experiments using real datasets demonstrate that, compared with two state-of-the-art solutions-RngMatch and LSRQ, our proposed PLRQ improves the query efficiency both by 2 orders of magnitude, and reduces the storage cost on Cloud Service Provider by about 79.5% and 73.6% respectively.

Deep Convolution Neural Networks (CNNs) have become the cornerstone of image classification, but the emergence of adversarial image attacks brings serious security risks to CNN-based applications. As a local perturbation attack, the border attack can achieve high success rates by only modifying the pixels around the border of an image, which is a novel attack perspective. However, existing border attacks have shortcomings in stealthiness and are easily detected. In this article, we propose a novel stealthy border attack method based on deep feature alignment. Specifically, we propose a deep feature alignment algorithm based on style transfer to guarantee the stealthiness of adversarial borders. The algorithm takes the deep feature difference between the adversarial and the original borders as the stealthiness loss and thus ensures good stealthiness of the generated adversarial images. To ensure high attack success rates simultaneously, we apply cross entropy to design the targeted attack loss and use margin loss as well as Leaky ReLU to design the untargeted attack loss. Experiments show that the structural similarity between the generated adversarial images and the original images is 8.8% higher than the state-of-art border attack method, indicating that our proposed adversarial images have better stealthiness. At the same time, the success rate of our attack in the face of defense methods is much higher, which is about four times that of the state-of-art border attack under the adversarial training defense.

Boolean range query (BRQ) as a typical type of spatial keyword query that is widely used in geographic information systems, location-based services and other applications. It retrieves the objects inside the query range and containing all query keywords. Many privacy-preserving BRQ schemes have been proposed to support BRQ over encrypted data. However, most of them fail to achieve efficient retrieval and lightweight result verification while suppressing access and search pattern leakage. Thus, in this paper, we propose an efficient verifiable privacy-preserving Boolean range query with suppressed leakage. Firstly, we convert BRQ into multi-keyword query by using Gray code and Bloom filter. Then, we achieve efficient oblivious multi-keyword query by combining distributed point function and PRP-based Cuckoo hashing, which protects the access and search patterns. Moreover, we support lightweight and oblivious result verification based on oblivious query, aggregate MAC, keyed-hashing MAC and XOR-homomorphic pseudorandom function. It enables query users to verify the result integrity with a proof whose size is independent of the size of the outsourced dataset. Finally, formal security analysis and extensive experiments demonstrate that our proposed scheme is adaptively secure and efficient for practical applications, respectively.

The utilization of blockchain technology as a distributed information sharing system has gained widespread adoption across various domains. However, its application to Flying Ad-Hoc Network (FANET), characterized by severe packet loss, poses significant challenges. The high packet loss rates in FANETs can result in decreased consensus success rates and negatively impact information sharing consistency and efficiency. In this paper, we proposed RoUBC, a novel consensus scheme for Flying Ad-Hoc Networks (FANET), which is based on the Raft protocol and is designed to address the challenges posed by the severe packet loss network in FANET. The proposed scheme consists of two phases: leader election and block consensus. In the leader election phase, we integrate multi-criteria decision-making and link prediction algorithms to design an efficient stable-leader election method. In the block consensus phase, we propose a dynamic block verification algorithm based on historical verification information to achieve efficient block consensus. Our theoretical analysis demonstrates that the proposed consensus protocol is safe and live, effectively ensuring the consistency of message sharing in FANET. Experiment results show that our scheme outperforms traditional Raft schemes, with 35% increase in consensus success rate and 25% improvement in consensus efficiency.

Symmetric Searchable Encryption (SSE), as an ideal primitive, can ensure data privacy while supporting retrieval over encrypted data. However, existing multi-user SSE schemes require the data owner to share the secret key with all query users or always be online to generate search tokens. While there are some solutions to this problem, they have at least one weakness, such as non-supporting conjunctive query, result decryption assistance of the data owner, and unauthorized access. To solve the above issues, we propose an O wner-free Di stributed S ymmetric searchable encryption supporting C onjunctive query (ODiSC). Specifically, we first evaluate the Learning-Parity-with-Noise weak Pseudorandom Function (LPN-wPRF) in dual-cloud architecture to generate search tokens with the data owner free from sharing key and being online. Then, we provide fine-grained conjunctive query in the distributed architecture using additive secret sharing and symmetric-key hidden vector encryption. Finally, formal security analysis and empirical performance evaluation demonstrate that ODiSC is adaptively simulation-secure and efficient.

In machine learning (ML), the massive data processing and dense computations based on matrices make outsourced inference computation a growing trend. The unreliability of cloud platforms makes privacy protection and inference correctness increasingly important in outsourced computations. Unfortunately, current works cannot provide an effective verification mechanism and privacy protection for outsourcing linear computing simultaneously. To address the issue, in the service architectures with malicious behaviors (such as curiosity, dishonesty, and collusion), we propose privacy-preserving and verifiable outsourcing inference computing (PPVLC) for the most fundamental linear computations in ML. PPVLC uses secret sharing and blinding techniques to protect privacy and achieve secure computation of matrix linear computation. Meanwhile, bilinear mapping based on matrix digest is utilized to verify computation correctness, ensuring the trustworthiness of the service. Security analysis and experiments demonstrate the reliability of our scheme and service efficiency.

The development of Industrial Internet of Things (IIoT) provides massive abundant data resources for trading and mining. However, the existing data trading schemes achieve data usage control at the cost of high latency, thereby resulting in poor service quality as the values of IIoT data degrade over time. This article proposes a monitor-based usage control model to enforce data usage policies on the user side, which eliminates frequent interactions between owners and users. Based on that, a data trading scheme with efficient usage control for IIoT (called DTSI) is devised, which utilizes blockchain smart contract and software guard extensions (SGX) to enable owners to fully control users’ identities and operations at minimal overhead. Security analysis shows that DTSI effectively prevents data abuse and ensures the fair exchange of data. Meanwhile, extensive experiments are conducted on the DTSI prototype comparing with the state-of-the-art schemes with real-world IIoT datasets, which demonstrates the efficiency of DTSI.

Location-based routing is a widely adopted message transmission mechanism in Vehicular Ad Hoc Networks (VANETs). While the existing location-based routing schemes of VANETs ignore the location privacy protection of vehicles, leading that the drivers to be tracked, and further threaten the safety of their life and property. To address the above issue, we propose an All-Round and Highly Privacy-Preserving Location-Based Routing for VANETs (called ARPLR). Specifically, ARPLR first proposes a road side unit assisted location management with location privacy protection that prevents the destination vehicle’s location from being leaked by the arbitrary query. Then, a message routing based on location ciphertext with highly privacy protection is designed by order revealing encryption, in which a multi-hop routing between the source and destination vehicle is established only by comparing the encrypted locations between intermediate vehicles. Security analysis shows that, ARPLR can not only effectively provide location privacy protection for the intermediate and the destination vehicles in the whole routing process, but also ensure end-to-end secure communication between the source and destination vehicles. Extensive experiments based on real-road map indicate that, compared with two state-of-the-art solutions, the average transmission delay of ARPLR is respectively reduced by about 18% and 60%, meanwhile the average packet delivery rate also increases about 30% and 2%, respectively.