Web services have brought great convenience to our daily lives. Meanwhile, they are vulnerable to Denial-of-Service (DoS) attacks. DoS attacks launched via vulnerabilities in the services can cause great harm. The vulnerabilities in protocol implementations are especially important because they are the keystones of web services. One vulnerable protocol implementation can affect all the web services built on top of it. Compared to the vulnerabilities that cause the target service to crash, resource exhaustion vulnerabilities are equally if not more important. This is because such vulnerabilities can deplete the system resources, leading to the unavailability of not only the vulnerable service but also other services running on the same machine. Despite the significance of this type of vulnerability, there has been limited research in this area.

When enjoying mobile crowdsensing (MCS), it is vital to evaluate the trustworthiness of mobile users (MUs) without disclosing their sensitive information. However, the existing schemes ignore this requirement in the multiple crowdsourcers (CSs) scenario. The lack of a credible sharing about MUs’ trustworthiness results in an inaccurate trust evaluation, disabling allocating tasks to reliable MUs. To address it, based on the analysis of the desired properties, we propose a scheme enablingdependabledata collection withmultiple crowdsourcerstrustsharing ($D^{2}MTS$). Specifically, we design the MU anonymous management. Two kinds of MU generated pseudonym systems without relationships are presented to mark each MU in trust evaluation and task execution, respectively. Through the devised pseudonym changes on these pseudonyms and the common token distribution algorithm,$D^{2}MTS$realizes privacy-preserving trust sharing. Moreover, to guarantee credible sharing, based on the hash chain,$D^{2}MTS$records MUs’ trustworthiness with the unforgeable signature on the blockchain established by multiple CSs which do not trust each other naturally. Extensive experiments show that compared with the other works,$D^{2}MTS$'s detection ratio of vicious MUs and the percentage of reliable MUs among the selected ones can increase by 208.61% and 28.27%. Both computational and communication delays are limited.

With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatial data is outsourced to the cloud server for reducing the local high storage and computing burdens, but at the same time causes security issues. Thus, extensive privacy-preserving spatial data query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) to encrypt data, but ASPE has proven to be insecure against known plaintext attack. And the existing schemes require users to provide more information about query range and thus generate a large amount of ciphertexts, which causes high storage and computational burdens. To solve these issues, based on enhanced ASPE designed in our conference version, we first propose a basic Privacy-preserving Spatial Data Query (PSDQ) scheme by using a new unified index structure, which only requires users to provide less information about query range. Then, we propose an enhanced PSDQ scheme (PSDQ$^+$) by using Geohash-based$R$-tree structure (called$GR$-tree) and efficient pruning strategy, which greatly reduces the query time. Formal security analysis proves that our schemes achieve Indistinguishability under Chosen Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our schemes are efficient in practice.

To motivate users' assistance for protecting others' location privacy by distributedK-anonymity in Location-Based Service (LBS), many incentive mechanisms have been proposed, where users obtain monetary compensation for their assistance. However, most existing distributedK-anonymity incentive mechanisms rely on trusted third parties and ignore users' malicious strategies, which destroys LBS's distributed structure as well as leads to users' privacy leakage and incentive ineffectiveness. To solve the above problems, we propose aPrivacy-Enhanced incentive mechAnism for distributedK-anonymity (PEAK). With determining the monetary transaction relationship and location transmission between users, PEAK enables the anonymous cloaking region construction without the trusted server. Meanwhile, PEAK devises role identification mechanism and accountability mechanism to restrain and punish malicious users, which protects users' location privacy and implements effective motivation on users' assistance. Theoretical analysis based on the game theory shows that PEAK constrains users' malicious strategies while satisfying individual rationality, computational efficiency, and satisfaction ratio. Extensive experiments based on the real-world dataset demonstrate that PEAK improves security and feasibility, especially reaching the success rate of anonymous cloaking region construction to more than 90$\%$and decreasing the malicious users' utilities significantly.

There is substantial attention to federated learning with its ability to train a powerful global model collaboratively while protecting data privacy. Despite its many advantages, federated learning is vulnerable to backdoor attacks, where an adversary injects malicious weights into the global model, making the global model's targeted predictions incorrect. Existing defenses based on identifying and eliminating malicious weights ignore the similarity variation of the local weights during iterations in the malicious model detection and the presence of benign weights in the malicious model during the malicious local weight elimination, resulting in a poor defense and a degradation of global model accuracy. In this paper, we defend against backdoor attacks from the perspective of local models. First, a malicious model detection method based on interpretability techniques is proposed. The method appends a sampling check after clustering to identify malicious models accurately. We further design a malicious local weight elimination method based on local weight contributions. This method preserves the benign weights in the malicious model to maintain their contributions to the global model. Finally, we analyze the security of the proposed method in terms of model closeness and then verify the effectiveness of the proposed method through experiments. In comparison with existing defenses, the results show that BADFL improves the global model accuracy by 23.14% while reducing the attack success rate to 0.04% in the best case.

With increasingly popular GPS-equipped mobile devices (e.g., smartphones, tablets, laptops), massive spatio-textual data has been outsourced to cloud servers for storage and analysis such as spatial keyword search. However, existing privacy-preserving spatial keyword query schemes only support coarse-grained non-temporal access control in single-user sharing scenarios, which does not scale well in time-related scenes such as message valid period. To solve the above issues, we propose Privacy-preserving Boolean Range Query with Temporal access control in mobile computing (PBRQ-T). Specifically, we first achieve PBRQ with linear search complexity using the adapted Gray code, Bloom filter, and Katz-Sahai-Waters encryption. Then, we provide fine-grained and temporal access control in PBRQ based on the forward/backward derivation function and attribute-based encryption, where PBRQ is executed only when the spatio-textual data is accessible. Finally, an enhanced PBRQ-T (i.e., PBRQ-T+) with faster-than-linear search complexity is proposed by constructing a Quadtree index structure. Our formal security analysis shows that data privacy and index privacy can be guaranteed during the query process. Our extensive experiments using a real-world dataset demonstrate the efficiency and feasibility of our schemes.

With the widespread development of intelligent systems, a considerable number of mobile devices are connected together, and continuously generate huge amounts of data. Although cloud storage provides perfect solution for effectively storing these massive data, how to ensure the integrity of the outsourced data becomes challenging. For this reason, the primitive of verifiable data streaming (VDS) protocol was introduced, and enables a data owner to continuously outsource streaming data to an untrusted cloud server, while capturing the integrity of the outsourced data. That is, when a data user retrieves some data item via its index from the server, he/she can publicly verify its integrity with the proof generated and returned by the server. Supporting data update is one of the major features of VDS, and allows the data owner to replace an old data item with a new one. Although many VDS protocols have been proposed to enhance the functionality and efficiency of the original VDS protocol, they all ignore the issue of preserving those updated data items. In fact, in various application scenarios of VDS, preserving and storing previously updated data items is actually necessary. For example, in the setting of DNA sequencing, there might be multiple versions of DNA fragments at the same location due to the genetic mutation. Obviously, for more precise treatment, all these DNA fragments need to be preserved. To this end, in this paper, we propose a VDS protocol that features of enabling the query of the update history of each data item. Specifically, we first put forward a new chameleon authentication tree with update history (UCAT), which consists of two CATs (the basic tree and the update history tree). In more detail, the basic tree is used to store the data item appended to the corresponding location for the first time, and the update history tree is utilized to preserve each updated version of the corresponding data item. Furthermore, based on UCAT, we propose a VDS protocol supporting update history queries, which allows a data user to retrieve any version of the data item. The theoretical analysis and performance evaluation indicate that our protocol outperforms previous ones in the field of functionality, and its computation/communication costs are acceptable. We also prove its security in the standard model.