Deep neural networks (DNNs) underpin critical applications yet remain vulnerable to backdoor attacks, typically reliant on heuristic brute-force methods. Despite significant empirical advancements in backdoor research, the lack of rigorous theoretical analysis limits understanding of underlying mechanisms, constraining attack predictability and adaptability. Therefore, we provide a theoretical analysis targeting backdoor attacks, focusing on how sparse decision boundaries enable disproportionate model manipulation. Based on this finding, we derive a closed-form ''ambiguous boundary region'' wherein negligible relabeled samples induce substantial misclassification. Influence function analysis further quantifies significant parameter shifts caused by these margin samples, with minimal impact on clean accuracy, formally grounding why such low poison rates suffice for efficacious attacks. Leveraging these insights, we propose Eminence, an explainable and robust black-box backdoor framework with provable theoretical guarantees and inherent stealth properties. Eminence optimizes a universal, visually subtle trigger that strategically exploits vulnerable decision boundaries and effectively achieves robust misclassification with exceptionally low poison rates (≤ 0.01%, compared to SOTA methods typically requiring ≥ 1 %). Comprehensive experiments validate our theoretical discussions and demonstrate the effectiveness of Eminence, confirming an exponential relationship between margin poisoning and adversarial boundary manipulation. Eminence maintains ≥ 90% attack success rate, exhibits negligible clean-accuracy loss, and demonstrates high transferability across diverse models, datasets and scenarios. Our code is available at https://github.com/NESA-Lab/Eminence

Social matching is a task that recommends potential friends to users based on their existing friendships. This user-to-user recommendation setting extends general recommender systems by shifting the focus from user-item interactions to interactions among users themselves, and general recommendation methods are often directly applied to this setting. However, we argue that in federated learning, directly applying existing federated recommendation methods to social matching can result in privacy leakage, due to the specificity of social matching: the mutual nature of friendships. We theoretically and quantitatively reveal these security vulnerabilities and propose a novel interaction-level membership inference attack targeting federated social matching. Our attack employs a reconstruction model combining affine transformations with an MLP to capture both linear and non-linear mappings between users' public and private embeddings. Trained on a portion of leaking user data, it can reconstruct benign users' private embeddings and infer their true friends. We conduct numerous experiments on multiple datasets under various experimental settings to verify the effectiveness and robustness of our attack method. Experimental results show that: (i) Our attack approach exhibits strong robustness and effectiveness across diverse experimental setups; (ii) Existing defense methods fail to effectively resist our attack while maintaining satisfactory recommendation performance. Our findings highlight the need for new privacy-preserving techniques specifically designed for federated social matching.

Cross-modal matching shows enormous potential to recognize objects across different sensory modalities, which is fundamental to numerous visual-language tasks like image-text retrieval and visual captioning. Existing works generally rely on massive and well-aligned data pairs for model training. Unfortunately, multimodal datasets are extremely difficult to annotate and collect. As an alternative, the co-occurred data pairs collected from the internet have been widely exploited to train a cross-modal matching model. However, the cheaply-collected dataset unavoidably contains mismatched pairs (i.e., noisy correspondence), which are detrimental to the matching model. In this paper, we propose an alternative method termed noisy correspondence rectification via Asymmetric Similarity Learning (ASL), and it allows for dealing with insufficient learning of positive and negative pairs caused by the popular triplet-based symmetric learning fashion. Specifically, the learning of positive or negative pairs within a triplet is conducted in an asymmetric fashion, and the self-paced weighting boundary is imposed on positive pairs to mitigate the effect of noise. Meanwhile, the optimization of negative samples will not be affected in the process of punishing potentially-noisy positive samples. To verify the effectiveness of our proposed approach, a series of experiments are conducted on three widely-used benchmarks (i.e., Flick30k, MS-COCO and CC152k), and the results show superior performance compared to the state-of-the-art methods.

The rapid expansion of decentralized finance (DeFi) applications has catalyzed the emergence of new blockchain systems at an unprecedented pace. However, these systems are largely evolving in isolation, hindering the development of a cohesive ecosystem where value and data can flow seamlessly across networks. Blockchain interoperability technologies are introduced to break down these communication barriers and facilitate effective interactions between different blockchain systems. In recent years, numerous approaches and solutions to blockchain interoperability have been proposed. While some reviews have attempted to categorize cross-chain solutions based on blockchain standards and architectures, a more in-depth analysis is warranted. In this work, we investigate mainstream cross-chain solutions from the perspective of their principles, applications, protocols, and performance. To clarify the concept of blockchain interoperability, we propose a conceptual model that characterizes both asset interoperability and data interoperability. Furthermore, we introduce a hierarchical architecture to categorize and analyze representative cross-chain solutions, covering both academic research and industrial implementations. To maximize the utility of this review for a wide audience, we also highlight open challenges and identify future directions in the field of blockchain interoperability, expecting to provide a comprehensive overview of cross-chain solutions.

In recent years, Building Information Modeling (BIM) has been widely used in the field of rail transit and plays an important role. Due to the numerous and complex elements of BIM file data, they are shared and used by multiple departments. Traditional BIMs that exist in the form of files are stored independently on centralized servers or local machines, lacking systematic management methods. Data update and maintenance are quite cumbersome. Besides, multi-departmental interaction and collaboration are inefficient and untrustworthy. Therefore, we propose a blockchain-based trusted sharing method for rail transit BIM data. First, a blockchain-based distributed BIM data sharing system architecture is proposed. Second, smart contracts are designed to achieve on-chain protection and shared use of digital resources such as rail transit BIM models. Third, the BIM data access control mechanism based on attribute-based encryption is proposed, and we implement a prototype of a trusted shared access system with permission control for multiple departments based on the InterPlanetary File System (IPFS) and the security mechanisms of Software Guard Extensions (SGX). Finally, the feasibility of the method is verified through scheme comparison, security analysis, and prototype system performance testing.

In SC'21, Alexandros Nikolaos Ziogas et al. proposed a Data-Centric Python workflow in their DaCe paper. DaCe provides high productivity, performance, and portability with language extensions and automatic optimizations. We reproduce the performance evaluation results from the paper on both CPU and GPU on the Azure CycleCloud cluster. We also reproduce the scaling results with up to 32 nodes and 64 processes. Our results show that the proposed workflow in that paper has outstanding performance and scalability in the provided cluster, in accordance with the SC paper.

As blockchain smart contracts become more widespread and carry more valuable digital assets, they become an increasingly attractive target for attackers. Over the past few years, smart contracts have been subject to a plethora of devastating attacks, resulting in billions of dollars in financial losses. There has been a notable surge of research interest in identifying defects in smart contracts. However, existing smart contract fuzzing tools are still unsatisfactory. They struggle to screen out meaningful transaction sequences and specify critical inputs for each transaction. As a result, they can only trigger a limited range of contract states, making it difficult to unveil complicated vulnerabilities hidden in the deep state space. In this paper, we shed light on smart contract fuzzing by employing a sequence-aware mutation and seed mask guidance strategy. In particular, we first utilize data-flow-based feedback to determine transaction orders in a meaningful way and further introduce a sequence-aware mutation technique to explore deeper states. Thereafter, we design a mask-guided seed mutation strategy that biases the generated transaction inputs to hit target branches. In addition, we develop a dynamic-adaptive energy adjustment paradigm that balances the fuzzing resource allocation during a fuzzing campaign. We implement our designs into a new smart contract fuzzer named MuFuzz, and extensively evaluate it on three benchmarks. Empirical results demonstrate that MuFuzz outperforms existing tools in terms of both branch coverage and bug finding. Overall, MuFuzz achieves higher branch coverage than state-of-the-art fuzzers (up to 25%) and detects 30 % more bugs than existing bug detectors.

Browsers are responsible for managing and interpreting the diverse data coming from the web. Despite the considerable efforts of developers, however, it is nearly impossible to completely eliminate potential vulnerabilities in such complicated software. While a family of fuzzing techniques has been proposed to detect flaws in web browsers, they still face the inherent challenge of generating test inputs with low semantic correctness and poor diversity. In this paper, we propose Tacoma, a novel fuzzing framework tailored for web browsers. Tacoma comprises three main modules: a semantic parser, a semantic aligner, and an input generator. By taking advantage of fine-grained semantic alignment techniques, Tacoma is capable of generating semantically correct test inputs, which significantly improve the probability of a fuzzer in triggering a deep browser state. In particular, by integrating a scope-aware strategy into input generation, Tacoma is able to deal with asynchronous code generation, thereby substantially increasing the diversity of the generated test inputs. We conduct extensive experiments to evaluate Tacoma on three production-level browsers, i.e., Chromium, Safari, and Firefox. Empirical results demonstrate that Tacoma outperforms state-of-the-art browser fuzzers in both achieving code coverage and detecting unique crashes. So far, Tacoma has identified 32 previously unknown bugs, 10 of which have been assigned CVEs. It is worth noting that Tacoma unearthed two bugs in Chromium that have remained undetected for ten years.

Bitcoin is one of the decentralized cryptocurrencies powered by a peer-to-peer blockchain network. Parties who trade in the bitcoin network are not required to disclose any personal information. Such property of anonymity, however, precipitates potential malicious transactions to a certain extent. Indeed, various illegal activities such as money laundering, dark network trading, and gambling in the bitcoin network are nothing new now. While a proliferation of work has been developed to identify malicious bitcoin transactions, the behavior analysis and classification of bitcoin addresses are largely overlooked by existing tools. In this paper, we propose BAClassifier, a tool that can automatically classify bitcoin addresses based on their behaviors. Technically, we come up with the following three key designs. First, we consider casting the transactions of the bitcoin address into an address graph structure, of which we introduce a graph node compression technique and a graph structure augmentation method to characterize a unified graph representation. Furthermore, we leverage a graph feature network to learn the graph representations of each address and generate the graph embeddings. Finally, we aggregate all graph embeddings of an address into the address-level representation, and engage in a classification model to give the address behavior classification. As a side contribution, we construct and release a large-scale annotated dataset that consists of over 2 million real-world bitcoin addresses and concerns 4 types of address behaviors. Experimental results demonstrate that our proposed framework outperforms state-of-the-art bitcoin address classifiers and existing classification models, where the precision and F1-score are 96% and 95%, respectively. Our implementation and dataset are released, hoping to inspire others.

Blockchain, a distributed and shared ledger, provides a credible and transparent solution to increase application auditability by querying the immutable records written in the ledger. Unfortunately, existing query APIs offered by the blockchain are inflexible and unscalable. Some studies propose off-chain solutions to provide more flexible and scalable query services. However, the query service providers (SPs) may deliver fake results without executing the real computation tasks and collude to cheat users. In this article, we propose a novel intelligent blockchain analytics platform termediQuery, in which we design a game theory based smart contract to ensure the trustworthiness of the query results at a reasonable monetary cost. Furthermore, the contract introduces the second opinion game that employs a randomized SP selection approach coupled with non-ordered asynchronous querying primitive to prevent collusion. We achieve a fixed price equilibrium, destroy the economic foundation of collusion, and can incentivize all rational SPs to act diligently with proper financial rewards. In particular,iQuerycan flexibly support semantic and analytical queries for generic consortium or public blockchains, achieving query scalability to massive blockchain data. Extensive experimental evaluations show thatiQueryis significantly faster than state-of-the-art systems. Specifically, in terms of the conditional, analytical, and multi-origin query semantics,iQueryis 2 ×, 7 ×, and 1.5 × faster than advanced blockchain and blockchain databases. Meanwhile, to guarantee 100% trustworthiness, only two copies of query results need to be verified iniQuery, whileiQuery's latency is$2 \sim 134$× smaller than the state-of-the-art systems.

Federated Recommendation (FR) has received con-siderable popularity and attention in the past few years. In FR, for each user, its feature vector and interaction data are kept locally on its own client thus are private to others. Without the access to above information, most existing poisoning attacks against recommender systems or federated learning lose validity. Benifiting from this characteristic, FR is commonly considered fairly secured. However, we argue that there is still possible and necessary security improvement could be made in FR. To prove our opinion, in this paper we present FedRecAttack, a model poisoning attack to FR aiming to raise the exposure ratio of target items. In most recommendation scenarios, apart from pri-vate user-item interactions (e.g., clicks, watches and purchases), some interactions are public (e.g., likes, follows and comments). Motivated by this point, in FedRecAttack we make use of the public interactions to approximate users' feature vectors, thereby attacker can generate poisoned gradients accordingly and control malicious users to upload the poisoned gradients in a well-designed way. To evaluate the effectiveness and side effects of FedRecAttack, we conduct extensive experiments on three real-world datasets of different sizes from two completely different scenarios. Experimental results demonstrate that our proposed FedRecAttack achieves the state-of-the-art effectiveness while its side effects are negligible. Moreover, even with small proportion (3%) of malicious users and small proportion (1%) of public interactions, FedRecAttack remains highly effective, which reveals that FR is more vulnerable to attack than people commonly considered.

Various attack methods against recommender systems have been proposed in the past years, and the security issues of recommender systems have drawn considerable attention. Traditional attacks attempt to make target items recommended to as many users as possible by poisoning the training data. Benifiting from the feature of protecting users' private data, federated recommendation can effectively defend such attacks. Therefore, quite a few works have devoted themselves to developing federated recommender systems. For proving current federated recommendation is still vulnerable, in this work we probe to design attack approaches targeting deep learning based recommender models in federated learning scenarios. Specifically, our attacks generate poisoned gradients for manipulated malicious users to upload based on two strategies (i.e., random approximation and hard user mining). Extensive experiments show that our well-designed attacks can effectively poison the target models, and the attack effectiveness sets the state-of-the-art.

Fuzzing is a technique of finding bugs by executing a target program recurrently with a large number of abnormal inputs. Most of the coverage-based fuzzers consider all parts of a program equally and pay too much attention to how to improve the code coverage. It is inefficient as the vulnerable code only takes a tiny fraction of the entire code. In this article, we design and implement an evolutionary fuzzing framework called V-Fuzz, which aims to find bugs efficiently and quickly in limited time for binary programs. V-Fuzz consists of two main components: 1) a vulnerability prediction model and 2) a vulnerability-oriented evolutionary fuzzer. Given a binary program to V-Fuzz, the vulnerability prediction model will give a prior estimation on which parts of a program are more likely to be vulnerable. Then, the fuzzer leverages an evolutionary algorithm to generate inputs which are more likely to arrive at the vulnerable locations, guided by the vulnerability prediction result. The experimental results demonstrate that V-Fuzz can find bugs efficiently with the assistance of vulnerability prediction. Moreover, V-Fuzz has discovered ten common vulnerabilities and exposures (CVEs), and three of them are newly discovered.

Following the principle of to set one's own spear against one's own shield, we study how to design adversarial completely automated public turing test to tell computers and humans apart (CAPTCHA) in this article. We first identify the similarity and difference between adversarial CAPTCHA generation and existing hot adversarial example (image) generation research. Then, we propose a framework for text-based and image-based adversarial CAPTCHA generation on top of state-of-the-art adversarial image generation techniques. Finally, we design and implement an adversarial CAPTCHA generation and evaluation system, called aCAPTCHA, which integrates 12 image preprocessing techniques, nine CAPTCHA attacks, four baseline adversarial CAPTCHA generation methods, and eight new adversarial CAPTCHA generation methods. To examine the performance of aCAPTCHA, extensive security and usability evaluations are conducted. The results demonstrate that the generated adversarial CAPTCHAs can significantly improve the security of normal CAPTCHAs while maintaining similar usability. To facilitate the CAPTCHA security research, we also open source the aCAPTCHA system, including the source code, trained models, datasets, and the usability evaluation interfaces.

Missing a security operation such as a bound check has been a major cause of security-critical bugs. Automatically checking whether the code misses a security operation in large programs is challenging since it has to understand whether the security operation is indeed necessary in the context. Recent methods typically employ cross-checking to identify deviations as security bugs, which collects functionally similar program slices and infers missed security operations through majority-voting. An inherent limitation of such approaches is that they heavily rely on a substantial number of similar code pieces to enable cross-checking. In practice, many code pieces are unique, and thus we may be unable to find adequate similar code snippets to utilize cross-checking.

Rare classes are usually hidden in an imbalanced dataset with the majority of the data examples from major classes. Rare-class mining (RCM) aims at extracting all the data examples belonging to rare classes. Most of the existing approaches for RCM require a certain amount of labeled data examples as input. However, they are ineffective in practice since requesting label information from domain experts is time consuming and human-labor extensive. Thus, we investigate the unsupervised RCM problem, which to the best of our knowledge is the first such attempt. To this end, we propose an efficient algorithm called Fast-RCM for unsupervised RCM, which has an approximately linear time complexity with respect to data size and data dimensionality. Given an unlabeled dataset, Fast-RCM mines out the rare class by first building a rare tree for the input dataset and then extracting data examples of the rare classes based on this rare tree. Compared with the existing approaches which have quadric or even cubic time complexity, Fast-RCM is much faster and can be extended to large-scale datasets. The experimental evaluation on both synthetic and real-world datasets demonstrate that our algorithm can effectively and efficiently extract the rare classes from an unlabeled dataset under the unsupervised settings, and is approximately five times faster than that of the state-of-the-art methods.

Demand response (DR) has been widely adopted as a strategic plan of the electricity market in maintaining power grid reliability, sustainability, and stability. In a typical emergency DR (EDR) that arises in colocation data centers, participating tenants can reduce their power consumption when the supply of electricity is a shortage and be rewarded with financial compensation. In this paper, we study a mechanism design problem of motivating tenants for colocation EDR (MEDR). To solve the MEDR problem, we present a truthful Fully Polynomial-Time Approximation Scheme (FPTAS) which is theoretically proved deterministic, truthful and near-optimal, and can be approximated within 1 + ϵ for any given ϵ > 0, while the running time is in the polynomial of the number of tenants n and ε. To speed up the calculation of the payments, we further study the Vickrey-Clarke-Groves (VCG) based mechanism. Moreover, we build a MEDR auction system (MEDRAS) and implement all mechanism algorithms for a colocation data center. Comprehensive and detailed experiments have been implemented to validate the efficiency of our proposed mechanisms.

Nowadays, the popularity of e-commerce has brought huge economic benefits to factories, third-party merchants, and e-commerce service providers. Driven by such huge economic benefits, malicious merchants attempt to promote items through inserting fraudulent purchases, fake review scores, and/or feedback, into them. Mitigating this threat is challenging due to the difficulty of obtaining internal e-commerce data, the variance of e-commerce services used by malicious merchants, and the reluctance of service providers in cooperation. In this paper, we present an efficient, platform-independent, and robust e-commerce fraud detection system, CATS, to detect frauds for different large-scale e-commerce platforms. We implement the design of CATS into a prototype system and evaluate this prototype on the world's popular e-commerce platform Taobao. The evaluation result on Taobao shows that CATS can achieve a high accuracy of 91% in detecting frauds. Based on this success, we then apply CATS on another large-scale e-commerce platforms, and again CATS achieves an accuracy of 96%, suggesting that CATS is very effective on real e-commerce platforms. Based on the cross-platform evaluation results, we conduct a comprehensive analysis on the reported frauds and reveal several abnormal yet interesting behaviors of those reported frauds. Our study in this paper is expected to shed light on defending against frauds for various e-commerce platforms.

Demand response (DR) is a vital means of electricity market in maintaining power grid reliability, sustainability and stability. DR can enable consumers (e.g. data centers) to reduce their electricity consumption when the supply of electricity is a shortage. The consumers will be rewarded if they reduce or shift some of their energy usage during peak hours. Aiming at solving the efficiency of DR, in this paper, we present MEDR, a mechanism on emergency DR in colocation data center. First, we formalize the MEDR problem and propose a dynamic programming to solve the optimization version of the problem. We then design a deterministic mechanism to solve the MEDR. We prove that our mechanism is truthful and it is an FPTAS, i.e., it can be approximated within 1 + ε for any given ε > 0, while the running time of our mechanism is polynomial in the number of tenants n and 1/ε. Furthermore, we also give an auction system covering the efficient FPTAS algorithm as bidding decision program for DR. Finally, we choose a real dataset to build a large number of simulation datasets in performance evaluation. The results show that our mechanism outperforms near-optimal and high utility demonstrate the effectiveness of our work.

In this paper, we study the impacts of non-Personal Identifiable Information (non-PII) on the privacy of graph data with attribute information (e.g., social networks data with users' profiles (attributes)), namely Structure-Attribute Graph (SAG) data, both theoretically and empirically. Our main contributions are two-fold: (i) we conduct the first attribute-based anonymity analysis for SAG data under both preliminary and general models. By careful quantification, we obtain the explicit correlation between the graph anonymity and the attribute information. We also validate our analysis through numerical and real world data-based evaluations and the results indicate that the non-PII can also lead to significant anonymity loss; and (ii) according to our theoretical analysis, we propose a new de-anonymization framework for SAG data, namely De-SAG, which takes into account both the graph structure and the attribute information to the best of our knowledge. By extensive experiments, we demonstrate that De-SAG can significantly improve the performance of state-of-the-art graph de-anonymization attacks. Our attribute-based anonymity analysis and de-anonymization framework are expected to provide data owners and researchers a more complete understanding on the privacy vulnerability of graph data, and thus shed light on future graph anonymization and de-anonymization research.

Hybrid-copy migration is a practical mean in cloud computing for memory intensive workload. But it does not perform perfectly in fetching remote pages. Aiming at solving this problem, we present a hot-page hybrid-copy migration method. We design a hot-page capturer to find out hot-pages, and a hot-page syringe to push hot-pages into transmission queue. We present an evaluation called page fault interval time, to evaluate the performance of hybrid-copy migration. The experimental results show that our method can extend the free part in page fault interval time about 19.79%, reduce the amount of remote page faults about 9.6%, and finally improve the performance of hybrid-copy migration.

Virtualization technology enables various application services to be distributed and encapsulated within virtual machines (VMs), which are dynamically allocated to physical machines (PMs) in cloud computing environments. However, in many existing virtualized systems, the limited network bandwidth often becomes a bottleneck resource, leading to the intensification of network competition and the performance degradation for communication or data intensive applications. Aiming at reducing communication overheads and improving the application performance, in this paper, we propose an Affinity-Aware Grouping method for Allocation of VMs (AAGA). Firstly, we identity and model the problem of affinity-aware grouping-based allocation for virtual machines, and propose a detailed grouping method based on which a heuristic bin packing algorithm is used to deploy VM groups into PMs. In order to demonstrate the effectiveness of AAGA, we create multiple real virtual clusters (multi-VCs) with 56 VMs running multi-VM applications and compare application performance with Non-Affinity-aware Grouping-based Allocation methods (NAGA). Experimental results show that AAGA achieves better performance than NAGA.

Virtualization technology is currently becoming increasingly popular and valuable in cloud computing environments due to the benefits of server consolidation, live migration, and resource isolation. Live migration of virtual machines can be used to implement energy saving and load balancing in cloud data center. However, to our knowledge, most of the previous work concentrated on the implementation of migration technology itself while didn't consider the impact of resource reservation strategy on migration efficiency. This paper focuses on the live migration strategy of multiple virtual machines with different resource reservation methods. We first describe the live migration framework of multiple virtual machines with resource reservation technology. Then we perform a series of experiments to investigate the impacts of different resource reservation methods on the performance of live migration in both source machine and target machine. Additionally, we analyze the efficiency of parallel migration strategy and workload-aware migration strategy. The metrics such as downtime, total migration time, and workload performance overheads are measured. Experiments reveal some new discovery of live migration of multiple virtual machines. Based on the observed results, we present corresponding optimization methods to improve the migration efficiency.

Virtualization technology has been widely applied in data centers and IT infrastructures, with advantages of server consolidation and live migration. Through live migration, data centers could flexibly move virtual machines among different physical machines to balance workloads, reduce energy consumption and enhance service availability.