The advancement of cloud computing and Internet of Things (IoT) has driven progress in image-based searchable encryption technology, which meets the escalating security demands of outsourced multimedia data in IoT scenarios. However, existing encrypted image retrieval schemes still face critical challenges, such as lack of cross-domain support, low retrieval efficiency and accuracy, and absence of reliable verifiability. To address these issues, this paper proposes a verifiable and privacy-preserving cross-domain image retrieval scheme (VPCDIR) in IoT. In our scheme, the re-encryption and key transformation technologies are implemented to achieve the availability of cross-domain image retrieval, and the learning with errors (LWE)-based enhanced secure k-nearest neighbor (kNN) algorithm is used to preserve the privacy of image features. Furthermore, the hybrid index mechanism that combines clustering and locality-sensitive hashing (LSH) is designed to enhance retrieval efficiency and accuracy, and the Merkle hash tree (MHT) with the short signature realizes reliable authenticity verification of the retrieval result. Finally, formal security analysis confirms the security of our scheme. Extensive experiments on the real dataset demonstrate the efficiency and practicability of VPCDIR for cross-domain image retrieval in IoT.

With the rapid advancement of data sharing technology, an increasing amount of data is being stored on cloud servers. To enable fine-grained access control over the data stored on cloud servers, the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) technology has been widely adopted. Recognizing that shared data and files often possess a hierarchical structure, hierarchical CP-ABE technology has been proposed recently. However, most existing schemes are restricted to single-domain data access, which limits their flexibility and universal applicability in practical applications. To address this limitation, an access control scheme based on hierarchical CP-ABE, named CDS-CP-ABE, is proposed to facilitate secure and efficient cross-domain data sharing. The scheme is capable of not only realizing fine-grained hierarchical access control within a single domain but also enabling cross-domain data sharing. Security analysis confirms that our scheme effectively resists chosen-plaintext attack. Furthermore, empirical results indicate that the time consumption associated with our scheme is lower compared to other existing schemes. • Achieve fine-grained hierarchical access control for data users. • Support cross-domain data access for data users based on their access levels. • Resist chosen-plaintext attack effectively, and reduce computational overhead compared to existing scheme.

Recently, vehicles have experienced a rise in networking and informatization, leading to increased security concerns. As the most widely used automotive bus network, the Controller Area Network (CAN) bus is vulnerable to attacks, as security was not considered in its original design. This paper proposes SIDuBzip2, a traffic anomaly detection method for the CAN bus based on the bzip2 compression algorithm. The proposed method utilizes the pseudo-periodic characteristics of CAN bus traffic, constructing time series of CAN IDs and calculating the similarity between adjacent time series to identify abnormal traffic. The method consists of three parts: the conversion of CAN ID values to characters, the calculation of similarity based on bzip2 compression, and the optimal solution of model parameters. The experimental results demonstrate that the proposed SIDuBzip2 method effectively detects various attacks, including Denial of Service , replay, basic injection, mixed injection, and suppression attacks. In addition, existing CAN bus traffic anomaly detection methods are compared with the proposed method in terms of performance and delay, demonstrating the feasibility of the proposed method.

Pervasive smartphones boost the prosperity of location-based service (LBS) and the increasing data prompt LBS providers to outsource their LBS datasets to the cloud side. The privacy issues of LBS in the outsourced cloud scenario have attracted considerable interest recently. However, current schemes cannot provide sufficient privacy preservation against practical challenges and are little concerned about the data retrieval efficiency of the cloud side. Therefore, we present an efficient Privacy-Preserving LBS Query Scheme (i.e., PPQS ). In our scheme, two cloud entities are employed to store the sensitive information of the outsourced data and provide the query service, which enhances the ability of privacy preservation for sensitive information. Besides, by using the techniques of homomorphic encryption and searchable symmetric encryption, the proposed scheme supports both the type query and the range query, which can significantly improve the data retrieval efficiency of the cloud side and reduce the computation burden on the cloud side and the user side. Through detailed analysis on security and computation cost, we show the enhanced ability of privacy preservation and the lower computation cost compared to previous schemes. Based on a real dataset, extensive simulations are performed to validate the effectiveness and performance of our scheme.

As a review system, the Crowd-Sourced Local Businesses Service System (CSLBSS) allows users to publicly publish reviews for businesses that include display name, avatar, and review content. While these reviews can maintain the business reputation and provide valuable references for others, the adversary also can legitimately obtain the user’s display name and a large number of historical reviews. For this problem, we show that the adversary can launch connecting user identities attack (CUIA) and statistical inference attack (SIA) to obtain user privacy by exploiting the acquired display names and historical reviews. However, the existing methods based on anonymity and suppressing reviews cannot resist these two attacks. Also, suppressing reviews may result in some reiews with the higher usefulness not being published. To solve these problems, we propose a cross-platform strong privacy protection mechanism (CSPPM) based on the partial publication and the complete anonymity mechanism. In CSPPM, based on the consistency between the user score and the business score, we propose a partial publication mechanism to publish reviews with the higher usefulness of review and filter false or untrue reviews. It ensures that our mechanism does not suppress reviews with the higher usefulness of reviews and improves system utility. We also propose a complete anonymity mechanism to anonymize the display name and avatars of reviews that are publicly published. It ensures that the adversary cannot obtain user privacy through CUIA and SIA. Finally, we evaluate CSPPM from both theoretical and experimental aspects. The results show that it can resist CUIA and SIA and improve system utility.