Remote sensing is considered to be a key technology for the development of smart cities. By analysing the data obtained by remote sensing, it is effective to manage and develop smart cities. However, with the rapid urbanisation and the increase in data capacity, how to store and retrieve remote sensing data which have more potential value and privacy with a more effective and safe method has become an imminent issue. In this paper, we propose an efficient verifiable and dynamic multi-keyword searchable encryption scheme for remote sensing data in smart cities. Moreover, we construct a double-chain index to support a multi-keyword search, where the Verification Index Chain is used to identify the users and the Keyword Index Chain is used to store the information of keywords. In addition, we adopt extended bitmap technology to realise update operations and verify the correctness and integrity of the search results. Experiments show that compared to the related existing schemes, the search time of the proposed scheme is reduced by 35% at most, the verification time is reduced by 14% at most and the update time is reduced by 57% at most.

The phrase searchable encryption scheme improves search efficiency and accuracy by searching a set of consecutive keywords from ciphertext. However, most current phrase searchable encryption schemes still don't support dynamic data updates in practical application scenarios such as smart healthcare due to forward security issues. In this article, a dynamic forward secure searchable encryption scheme with phrase search for smart healthcare systems is proposed. On the one hand, we adopt a state chain structure to construct an inverted index, which contains the location of keywords so as to achieve phrase search. On the other hand, we have added a modification operation that modifies the location of keywords directly by traversing the inverted index to achieve indexes update. In this way, the efficiency of data updating can be raised greatly because the update operation of first-delete-then-add is avoided in the previous phrase search scheme. The scheme is proved to satisfy the forward security in the leaked search pattern and access pattern since the server cannot know the updated state of updated files in the state chain index structure which is randomly generated by the client. Experimental results show that the proposed scheme has higher search accuracy and efficiency which can save at least 50ms in search time compared to other related schemes.

Group signatures are active cryptographic topics where group members are granted right to sign messages anonymously on behalf of their group. However, in practical applications, such rights are not permanent in most cases and are usually limited to some time periods. This means that the signing right of each group member needs to be associated with time periods such that it can be automatically changed with the latter. Among the known approaches, verifier local revocation (VLR) seems to be the feasible one to implement the above functionality, but it will cause an inefficient verification process when the group size is large. In this paper, we describe a group signature scheme with time-bound keys, based on the hardness of lattice assumption, which implements the limitation of the signing right to any time period by constructing a lattice-based redactable signature scheme. Our scheme still adds VLR mechanism for some members who need to revoke prematurely, but the time-bound keys function ensures such members are only a small fraction that do not incur excessive cost for revocation check. We give implementation for our scheme under 93-bit and 207-bit security respectively to demonstrate the practicability – all costs are independent of the group size and achieve a relatively efficient level.

In an intelligent edge network, data owners encrypt data and outsource it to the edge servers to prevent the leakage of data and user information. It is a research issue to achieve efficient search and data update of the ciphertext stored in the edge servers. For the above problems, we construct a fuzzy multi-keyword search scheme based on a two-level tree index, where the first-level tree index stores keyword tags and the second-level stores encrypted file identifiers and counting bloom filters (CBFs). By constructing a two-level tree index, efficient multi-keyword search is realised, and it also has obvious time advantages when performing the single-keyword search. In addition, the proposed scheme supports index updating by introducing the CBF, and it can realise fuzzy search by calculating the inner product between the CBF in index and search trapdoor. At last, the proposed scheme is proved to be semantically secure under the known ciphertext attack model. Theoretical analysis and simulation test indicate that the proposed scheme spends lower computation overhead than other related schemes, especially in the search phase; as the number of matching files or query keywords grows, it costs less time, at around 2–10 ms.

Abstract With the increasing number of fibre access users, P2P file sharing download traffic has gradually become one of the enormous bandwidth consumptions for the access network. Localizing traffic could save bandwidth and reduce latency, but it caused optical line terminal (OLT) overload problems. A cloud‐edge collaboration technology (IRS‐CECT) based P2P redirection strategy is proposed for this problem. OLT assumes the role of the cloud computing centre in the strategy. Optical network unit (ONU) serves as an edge computing node to share traffic load for the cloud computing centre. ONU intercepts the upstream query packets for OLT. This strategy can reduce the problem of OLT overload caused by increasing users. Simulation results indicate that the IRS‐CECT strategy can reduce the delay by nearly half and reduce the traffic load on the cloud computing centre OLT.

Recently, security policies and behaviour detection methods have been proposed to improve the security of blockchain by many researchers. However, these methods cannot discover the source of typical behaviours, such as the malicious applications in the blockchain environment. Android application is an important part of the blockchain operating environment, and machine learning-based Android malware application detection method is significant for blockchain user security. The way of constructing features in these methods determines the performance. The single-feature mechanism, training classifiers with one type of features, cannot detect the malicious applications effectively which exhibit the typical behaviours in various forms. The multifeatures fusion mechanism, constructing mixed features from multiple types of data sources, can cover more kinds of information. However, different types of data sources will interfere with each other in the mixed features constructed by this mechanism. That limits the performance of the model. In order to improve the detection performance of Android malicious applications in complex scenarios, we propose an Android malicious application detection method which includes parallel feature processing and decision mechanism. Our method uses RGB image visualization technology to construct three types of RGB image which are utilized to train different classifiers, respectively, and a decision mechanism is designed to fuse the outputs of subclassifiers through weight analysis. This approach simultaneously extracts different types of features, which preserve application information comprehensively. Different classifiers are trained by these features to guarantee independence of each feature and classifier. On this basis, a comprehensive analysis of many methods is performed on the Android malware dataset, and the results show that our method has better efficiency and adaptability than others.

Although most existing linkable ring signature schemes on lattice can effectively resist quantum attacks, they still have the disadvantages of excessive time and storage overhead. This paper constructs an identity-based linkable ring signature (LRS) scheme over NTRU lattice by employing the technologies of trapdoor generation and rejection sampling. The security of this scheme relies on the small integer solution (SIS) problem on NTRU lattice. We prove that this scheme has unconditional anonymity, unforgeability, and linkability under the random oracle model (ROM). Through the performance analysis, this scheme has a shorter size of public/private keys, and when the number of ring members is small (such as N ≤ 8 ), this scheme has a shorter signature size compared with other existing latest lattice-based LRS schemes. The computational efficiency of signature has also been further improved since it only involves multiplication in the polynomial ring and modular operations of small integers. Finally, we implemented our scheme and other similar schemes, and it is shown that the time for the signature generation and verification of this scheme decreases roughly by 44.951% and 33.503%, respectively.

The rapid development of the Internet leads to a surge in the amount of information transmission and brings many security problems. For multimedia information transmission, especially digital images, it is necessary to compress and encrypt at the same time. The emergence of compressive sensing solves this problem. Compressive sensing can compress and encrypt at the same time, which can not only reduce the transmission bandwidth of the network but also improve the security of the system. However, when using compressive sensing encryption, the whole measurement matrix needs to be stored, and the compressive sensing can be combined with a chaotic system, so only the generation parameters of the matrix need to be stored, and the security of the system can be further improved by using the sensitivity of the chaotic system. This paper introduces a secure and efficient image compression-encryption scheme using a new chaotic structure and compressive sensing. The chaotic map used in the scheme is generated by our new and universal chaotic structure, which not only expands the chaotic range of the chaotic system but also improves the performance of the chaotic system. After analyzing the performance comparison of traditional one-dimensional chaotic maps and some existing methods, the image compression-encryption scheme based on a new chaotic structure and compressive sensing has a good encryption effect and large keyspace, which can resist brute force attack and statistical attack.

Identity‐based fully homomorphic encryption (IBFHE) provided a fundamental solution to the problem of huge public key size that exposed in fully homomorphic encryption (FHE) schemes, thus it is significant to make FHE become more practical. In recent years, the construction of IBFHE schemes were mainly based on lattices due to their conjectured resistance against quantum cryptanalysis, however, which makes these cryptosystems further unpractical. The main reason is the trapdoor function on which the scheme was based is rather complex for practical and the ciphertext size is too large. In this study, the authors propose an efficient batch IBFHE scheme, which can be proven secure from the standard LWE assumption in the standard model. The first contribution of this work is that the authors construct an efficient batch version of MP12 preimage sampling algorithm, which can efficiently generate identity keys for multi‐bit IBE schemes. Based on that, the authors construct an asymptotically‐faster multi‐bit IBE scheme as the second contribution. The third contribution is that the authors transform the multi‐bit IBE scheme to batch IBFHE scheme which supports to encrypt any message in . Compared with the similar schemes, the authors show their schemes are essentially improved.

Buzzwords are the main embodiment of Internet culture, which play an important role in public opinion analysis, social focus tracking and language evolution study. At present, questionnaire has been wildly used as a standard method to obtain network buzzwords, which is subjective and costly. In this paper, we will propose a novel algorithm relying on the time-distribution feature of words and a KL-divergence measure to estimate words' popularity so as to figure out buzzwords in a specific period. The time-distribution feature simply states the fact that buzzwords' usage has a sharp increase during a very short period, which is then modeled formally with the KL-divergence measure. Compared with traditional method involving much workforce, the automatic algorithm presented here is clearly more efficient. Moreover, buzzwords identified in this manner will not be affected by individual's subjective opinions, so they can reflect the language usage in practice better. When applying the algorithm to a social media big data set, our experimental results show that the proposed approach can accurately identify buzzwords in a certain period, which is highly coincident with results tagged manually.