In this paper, we propose BiTDB, a built-in Trusted Execution Environment (TEE) database for embedded systems, to realize higher system availability while ensuring data confidentiality. With BiTDB, dilemmas that the state-of-the-art research work on secure embedded databases has to face can be significantly reduced and eliminated, including (i) complicated research and realization on searchable encryption algorithms (SEA), (ii) limited support to all database operations, and (iii) almost none of specific design and optimizations toward built-in TEE embedded databases. Through BiTDB, all database operations can process plaintext in TEE instead of retrieving ciphertext by developing complicated SEAs. To enable BiTDB to handle database files in Rich Execution Environment (REE) as local ones, we extend the TEE OS with generic file I/O libraries. Then, we contribute three critical optimizations to significantly reduce redundant memory and file operations between TEE and REE, and BiTDB achieve better system performance and availability in embedded systems. Finally, we have implemented the prototype system based on OP-TEE and SQLite for several typical platforms, including virtualization and hardware environments. The TPC-H test shows BiTDB can achieve 85% (on average) of the original database performance while guaranteeing data confidentiality and integrity.

In recent years, the widely collected spatial-textual data has given rise to numerous applications centered on spatial keyword queries. However, securely providing spatial keyword query services in an outsourcing environment has been challenging. Existing schemes struggle to enable top- k spatial keyword queries on encrypted data while hiding search, access, and volume patterns, which raises concerns about availability and security. To address the above issue, this paper proposes OBIR-tree, a novel index structure for oblivious (provably hides search, access, and volume patterns) top- k spatial keyword queries on encrypted data. As a tight spatial-textual index tailored from the IR-tree and PathORAM, OBIR-tree can support sublinear search without revealing any useful information. Furthermore, we present extension designs to optimize the query latency of the OBIR-tree: (1) combine the OBIR-tree with hardware secure enclaves ( e.g., Intel SGX) to minimize client-server interactions; (2) build a Real/Dummy block Tree (RDT) to reduce the computational cost of oblivious operations within enclaves. Extensive experimental evaluations on real-world datasets demonstrate that the search efficiency of OBIR-tree outperforms state-of-the-art baselines by 25x ~ 723× and is practical for real-world applications.

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.

In this paper, we propose BiTDB, a built-in Trusted Execution Environment (TEE) database for embedded systems, to realize higher system availability while ensuring data confidentiality. With BiTDB, dilemmas that the state-of-the-art research work on secure embedded databases has to face can be significantly reduced and eliminated, including (i) complicated research and realization on searchable encryption algorithms (SEA), (ii) limited support to all database operations, and (iii) almost none of specific design and optimizations toward build-in TEE embedded databases. Through BiTDB, all database operations can process plaintext in TEE instead of retrieving ciphertext by developing complicated SEAs. To enable BiTDB to handle database files in Rich Execution Environment (REE) as local ones, we extend the TEE OS with generic file I/O libraries. Then, we contribute three critical optimizations to significantly reduce redundant memory and file operations between TEE and REE, and BiTDB achieve better system performance and availability in embedded systems. Finally, we have implemented the prototype system based on OP-TEE and SQLite for several typical platforms, including virtualization and hardware environments. The TPC-H test shows BiTDB can achieve 85% (on average) of the original database performance while guaranteeing data confidentiality and integrity. Our project repository is athttps://github.com/CharlieMCY/BiTDB.

Federated Learning (FL) suffers from low convergence and significant accuracy loss due to local biases caused by non-Independent and Identically Distributed (non-IID) data. To enhance the non-IID FL performance, a straightforward idea is to leverage the Generative Adversarial Network (GAN) to mitigate local biases using synthesized samples. Unfortunately, existing GAN-based solutions have inherent limitations, which do not support non-IID data and even compromise user privacy. To tackle the above issues, we propose a GAN-based unbiased FL scheme, calledFlGan, to mitigate local biases using synthesized samples generated by GAN while preserving user-level privacy in the FL setting. Specifically,FlGanfirst presents a federated GAN algorithm using the divide-and-conquer strategy that eliminates the problem of model collapse in non-IID settings. To guarantee user-level privacy,FlGanthen exploits Fully Homomorphic Encryption (FHE) to design the privacy-preserving GAN augmentation method for the unbiased FL. Extensive experiments show thatFlGanachieves unbiased FL with$10\%-60\%$accuracy improvement compared with two state-of-the-art FL baselines (i.e., FedAvg and FedSGD) trained under different non-IID settings. The FHE-based privacy guarantees only cost about 0.53% of the total overhead inFlGan.

Anomaly traffic detection is a crucial issue in the cyber-security field. Previously, many researchers regarded anomaly traffic detection as a supervised classification problem. However, in real scenarios, anomaly network traffic is unpredictable, dynamically changing and difficult to collect. To address these limitations, we employ anomaly detection setting to propose a novel semi-supervised anomaly network traffic detection framework. It only learns features of normal samples during the training phase. Our framework utilizes low-pass filtering to extract multi-scale low-frequency information from 2-D traffic image. Furthermore, we design a two-stage fusion scheme to incorporate information from original and multi-scale low-frequency traffic image modalities. We conduct experiments on two public datasets: ISCX Tor-nonTor and USTC-TFC2016. The experimental results show that our method outperforms current state-of-the-art anomaly detection methods.

Recently, the explosive development of unmanned aerial vehicles (UAVs) promotes its wide application in various services such as package delivery, traffic monitoring. However, due to the high-speed movement, current UAVs usually adopts the unsecure channel without the complicated authentication mechanism to ensure real-time communication. In this paper, we introduce a certificateless authentication scheme with fuzzy batch verification (CLFBV) to achieve once-for-all verification of parallel messages and ensure the real-time secure communication of UAVs. CLBFV defines the error tolerance property for authenticated communication, which allows a tolerance threshold for the messages that are unable to pass authentication. In addition, our proposed scheme is proved to be secure and existentially unforgeable under the chosen message attack and fuzzy identity attack in the random oracle model. The efficiency analysis shows that CLBFV is more efficient and feasible than other existing batch verification schemes.

With the popularity of image editing tools, the originality and information security of images are facing serious threats. The most common threat is splicing forgery that copies a part of the area from one donor image to the acceptor one. Some research works were proposed to protect the image originality, whereas they are still difficult to apply in practice. There are two main reasons: (a) very limited data for learning models; (b) huge attribute differences between the donor and acceptor images. We propose two novel tasks to conquer the above challenges: Synthetic Adversarial Networks (SANs) and Hybrid Dense U-Net (HDU-Net). SAN finds the most secluded position for inserting tampered areas in an image by learning the association between scenes and objects, and can enlarge the original small data set by more than 40 times. We call the data set SF-Data generated by SAN. We combine the dense U-Net that detects the differences of the essential attributes of image with four spaces containing more available feature information to propose HDU-Net. Then, the synthetic data set SF-Data are used to train HDU-Net. We perform various attack experiments on several public data sets to demonstrate the effectiveness and robustness of our method.

Graph embedding converts a graph into a multi-dimensional space in which the graph structural information or graph properties are maximumly preserved. It is an effective and efficient way to provide users a deeper understanding of what is behind the data and thus can benefit a lot of useful applications. However, most graph embedding methods suffer from high computation and space costs. In this paper, we present a simple graph embedding method that directly embeds the graph into its Euclidean distance space. This method does not require the learned representations to be low dimensional, but it has several good characteristics. We find that the centrality of nodes/edges can be represented by the position of nodes or the length of edges when a graph is embedded. Besides, the edge length is closely related to the density of regions in a graph. We then apply this graph embedding method into graph analytics, such as community detection, graph compression, and wormhole detection, etc. Our evaluation shows the effectiveness and efficiency of this embedding method and contends that it yields a promising approach to graph analytics.

Dynamic Searchable Symmetric Encryption (DSSE) has gained increasing popularity as it enables users to perform both file updates and ciphertext retrieval over encrypted data . However, existing DSSE schemes still lead to privacy leakage ( e.g., forward and backward privacy) in the dynamic setting. Some forward and backward secure DSSE schemes have been proposed, but still cannot support the keyword shielding flexibly. To solve this challenging issue, we propose a Forward and Backward Authorized Keyword Search (FB-AKS) scheme with recoverable keyword shielding by using trapdoor permutations and puncturable encryption in this paper. Compared with existing forward and backward private schemes, FB-AKS achieves keyword authorization flexibly ( e.g., keyword shielding, keyword un-shielding). The formal security analysis proves that FB-AKS achieves forward and backward security. And extensive experiments demonstrate that FB-AKS has less computation and storage overheads .

In this paper, we incorporate the realistic scenario of key protection into link privacy preserving and propose the target-link privacy preserving (TPP) model: target links referred to as targets are the most important and sensitive objectives that would be intentionally attacked by adversaries, in order that need privacy protections, while other links of less privacy concerns are properly released to maintain the graph utility. The goal of TPP is to limit the target disclosure by deleting a budget limited set of alternative non-target links referred to as protectors to defend the adversarial link predictions for all targets. Traditional link privacy preserving treated all links as targets and concentrated on structural level protections in which serious link disclosure and high graph utility loss is still the bottleneck of graph releasing today, while TPP focuses on the target level protections in which key protection is implemented on a tiny fraction of critical targets to achieve better privacy protection and lower graph utility loss. Currently there is a lack of clear TPP problem definition, provable optimal or near optimal protector selection algorithms and scalable implementations on large-scale social graphs. Firstly, we introduce the TPP model and propose a dissimilarity function used for measuring the defense ability against privacy analyzing for the targets. We consider two different problems by budget assignment settings: 1) we protect all targets and to optimize the dissimilarity of all targets with a single budget; 2) besides the protections of all targets, we also care about the protection of each target by assigning a local budget to every target. Moreover, we propose two local protector selections, namely cross-target and with-target pickings. Each problem with each protector picking selection is corresponding to a greedy algorithm. We also implement scalable implementations for all greedy algorithms by limiting the selection scale of protectors, and we prove that all greedy-based algorithms achieve approximation by holding the monotonicity and submodularity. Through experiments on large real social graphs, we demonstrate the effectiveness and efficiency of the proposed target link protection methods.

The disclosure of an important yet sensitive link may cause serious privacy crisis between two users of a social graph. Only deleting the sensitive link referred to as a target link which is often the attacked target of adversaries is not enough, because the adversarial link prediction can deeply forecast the existence of the missing target link. Thus, to defend some specific adversarial link prediction, a budget limited number of other non-target links should be optimally removed. We first propose a path-based dissimilarity function as the optimizing objective and prove that the greedy link deletion to preserve target link privacy referred to as the GLD2Privacy which has monotonicity and submodularity properties can achieve a near optimal solution. However, emulating all length limited paths between any pair of nodes for GLD2Privacy mechanism is impossible in large scale social graphs. Secondly, we propose a Walk2Privacy mechanism that uses self-avoiding random walk which can efficiently run in large scale graphs to sample the paths of given lengths between the two ends of any missing target link, and based on the sampled paths we select the alternative non-target links being deleted for privacy purpose. Finally, we compose experiments to demonstrate that the Walk2Privacy algorithm can remarkably reduce the time consumption and achieve a very near solution that is achieved by the GLD2Privacy.

Differential privacy (DP) is a promising tool for preserving privacy during data publication, as it provides strong theoretical privacy guarantees in face of adversaries with arbitrary background knowledge. Histogram, as the result of a set of count queries, serves as a core statistical tool to report data distributions and is in fact viewed as the fundamental method for many other statistical analysis such as range queries. It is an important form for data publishing. In this paper, we consider the scenario of publishing sensitive histogram data with differential privacy scheme. Existing work in this field has justified that, comparing to directly applying differential privacy techniques (i.e., injecting noise) over the counts in histogram bins, grouping bins before noise injection is more effective (i.e., with higher utility) as it introduces much less error over the sanitized histogram given the same privacy budget. However, state-of-the-art works have not unveiled how the overall utility of a sanitized histogram can be affected by the balance between the privacy budget distributed between grouping and noise injection phases. In this work, we conducted a theoretical study towards how the probability of getting better groups can be improved such that the overall error introduced in sanitized histogram can be further reduced, which directly leads to a higher utility of the sanitized histogram. In particular, we show that the probability of achieving better grouping can be affected by two factors, namely privacy budget assigned in grouping and the normalized utility function used for selecting groups. Motivated by that, we propose a new DP histogram publishing scheme, namely IHP (Iterative Histogram Partition), in which we carefully assign privacy budget between grouping and injection phases based on our theoretical study. We also theoretically prove that e-differential privacy can be achieved according to our new scheme. Moreover, we also show that, under the same privacy budget, our scheme exhibits less errors in the sanitized histograms comparing with state-of-the-art methods. Finally, empirical study over three real-world datasets also justifies that our scheme achieves the least error among series of state-of-the-art baseline methods.

State-of-the-art classical influence maximization (IM) techniques are "competition-unaware" as they assume that a group (company) finds seeds (users) in a network independent of other groups who are also simultaneously interested in finding such seeds in the same network. However, in reality several groups often compete for the same market (e.g., Samsung, HTC, and Apple for the smart phone market) and hence may attempt to select seeds in the same network. This has led to increasing body of research in devising IM techniques for competitive networks. Despite the considerable progress made by these efforts toward finding seeds in a more realistic settings, unfortunately, they still make several unrealistic assumptions (e.g., a new company being aware of a rival's strategy, alternate seed selection, etc.) making their deployment impractical in real-world networks. In this paper, we propose a novel framework based on game theory to provide a more realistic solution to the IM problem in competitive networks by jettisoning these unrealistic assumptions. Specifically, we seek to find the "best" IM strategy (an algorithm or a mixture of algorithms) a group should adopt in the presence of rivals so that it can maximize its influence. As each group adopts some strategy, we model the problem as a game with each group as competitors and the expected influences under the strategies as payoffs. We propose a novel algorithm called GetReal to find each group's best solution by leveraging the competition between different groups. Specifically, it seeks to find whether there exist a Nash Equilibrium (NE) in a game, which guarantees that there exist an "optimal" strategy for each group. Our experimental study on real-world networks demonstrates the superiority of our solution in a more realistic environment.