Some smart contracts violate decentralization principles by defining privileged accounts that manage other users' assets without permission, introducing centralized risks that have caused financial losses. Existing methods, however, face challenges in accurately detecting diverse centralized risks due to their dependence on predefined behavior patterns. In this paper, we propose JANUS, an automated analyzer for Solidity smart contracts that detects financial centralized risks independently of their specific behaviors. JANUS identifies differences between states reached by privileged and ordinary accounts, and analyzes whether these differences are finance-related. Focusing on the impact of risks rather than behaviors, JANUS achieves improved accuracy compared to existing tools and can uncover centralized risks with unknown patterns. To evaluate JANUS's performance, we compare it with other tools using a dataset of 540 contracts. Our evaluation demonstrates that JANUS outperforms representative tools in terms of detection accuracy for financial centralized risks. Additionally, we evaluate JANUS on a real-world dataset of 33,151 contracts, successfully identifying two types of risks that other tools fail to detect. We also prove that the state traversal method and variable summaries, which are used in JANUS to reduce the number of states to be compared, do not introduce false alarms or omissions in detection.

WebAssembly (WASM) has emerged as a crucial technology in smart contract development for several blockchain platforms. Unfortunately, since their introduction, WASM smart contracts have been subject to several security incidents caused by contract vulnerabilities, resulting in substantial economic losses. However, existing tools for detecting WASM contract vulnerabilities have accuracy limitations, one of the main reasons being the coarse-grained emulation of the on-chain data APIs. In this article, we introduce WACANA, an analyzer for WASM contracts that accurately detects vulnerabilities through fine-grained emulation of on-chain data APIs. WACANA precisely simulates both the structure of on-chain data tables and their corresponding API functions, and integrates concrete and symbolic execution within a coverage-guided loop to balance accuracy and efficiency. Evaluations on a vulnerability dataset of 2,012 contracts show WACANA outperforming state-of-the-art tools in accuracy. Further validation on 5,602 real-world contracts confirms WACANA’s practical effectiveness.

Combinatorial optimization (CO) is a fundamental tool in many fields. Many neural combinatorial optimization (NCO) methods have been proposed to solve CO problems. However, existing NCO methods typically require significant computational and storage resources, and face challenges in maintaining robustness to distribution shifts between training and test data. To address these issues, we model CO instances into probability measures, and introduce Wasserstein-based metrics to quantify the difference between CO instances. We then leverage a popular data compression technique, \emph{coreset}, to construct a small-size proxy for the original large dataset. However, the time complexity of constructing a coreset is linearly dependent on the size of the dataset. Consequently, it becomes challenging when datasets are particularly large. Further, we accelerate the coreset construction by adapting it to the merge-and-reduce framework, enabling parallel computing. Additionally, we prove that our coreset is a good representation in theory. {Subsequently}, to speed up the training process for existing NCO methods, we propose an efficient training framework based on the coreset technique. We train the model on a small-size coreset rather than on the full dataset, and thus save substantial computational and storage resources. Inspired by hierarchical Gonzalez’s algorithm, our coreset method is designed to capture the diversity of the dataset, which consequently improves robustness to distribution shifts. Finally, experimental results demonstrate that our training framework not only enhances robustness to distribution shifts but also achieves better performance with reduced resource requirements.

Deep learning has emerged as a promising technology for achieving Android malware detection. To further unleash its detection potentials, software visualization can be integrated for analyzing the details of app behaviors clearly. However, facing increasingly sophisticated malware, existing visualization-based methods, analyzing from one or randomly-selected few views, can only detect limited attack types. We propose and implement LensDroid, a novel technique that detects Android malware by visualizing app behaviors from multiple complementary views. Our goal is to harness the power of combining deep learning and software visualization to automatically capture and aggregate high-level features that are not inherently linked, thereby revealing hidden maliciousness of Android app behaviors. To thoroughly comprehend the details of apps, we visualize app behaviors from three related but distinct views of behavioral sensitivities, operational contexts and supported environments. We then extract high-order semantics based on the views accordingly. To exploit semantic complementarity of the views, we design a deep neural network based model for fusing the visualized features from local to global based on their contributions to downstream tasks. A comprehensive comparison with six baseline techniques is performed on datasets of more than 51K apps in three real-world typical scenarios, including overall threats, app evolution and zero-day malware. The experimental results show that the overall effectiveness of LensDroid is better than the baseline techniques. We also validate the complementarity of the views and demonstrate that the multi-view fusion in LensDroid enhances Android malware detection.

Cooking is one of the oldest and the most common human activities in everyone’s daily life. Instructional cooking videos have also become one of the most common data sources for multimodal visual understanding researches. Compared to other domains, multimodal cooking videos: 1. not only have significantly stronger cross-modal dependencies between the speech transcriptions and their semantically-aligned visual frames at static time stamps; 2. but also have significantly stronger cross-context dependencies among the sequential steps along the temporal dimension, resulting as an ideal domain for contextualized semantic understanding. We propose Cooking-CLIP, which introduces the concept of language-image pretraining (CLIP) from a general-purpose multimodal embedding problem into a customized recipe generation application. We also propose a context-aware pretraining approach, to facilitate a better CLIP customization to cooking-related applications. Our approach achieves higher text generation accuracies than a strong zero-shot baseline on two instructional cooking video data sets, CrossTask and YouCook2. We also achieve comparative accuracies against a fully-supervised approach, with only a narrow difference, in spite of our zero-shot setting.

Current formal approaches have been successfully used to find design flaws in many security protocols. However, it is still challenging to automatically analyze protocols due to their large or infinite state spaces. In this paper, we propose SmartVerif, a novel and general framework that pushes the limit of automation capability of Tamarin, a state-of-the-art protocol verifier. The primary technical contribution is thedynamicstrategy inside SmartVerif, which can be used to smartly search proof trees. Different from the existing static strategies, our dynamic strategy can automatically optimize itself according to the security protocols without any human intervention. We implement the strategy by modifying Tamarin and introducing a reinforcement learning algorithm to avoid non-terminating paths in the proof tree. Besides, to improve SmartVerif, we add multiple extracted information for training the reinforcement learning network and design a submodule of Non-termination Estimation to collect training data precisely and rapidly. Experimental results show that SmartVerif can automatically verify all security protocols studied in this paper. The case study validates the efficiency of our dynamic strategy. The experimental results also demonstrate the effectiveness of our extracted information, and the accuracy of the submodule of Non-termination Estimation.

This paper proposes CookingCLIP, which introduces the latest CLIP (Contrastive Language-Image Pre-training) embedding from the general domain into the specific domain of cooking understanding, and makes two adaption upon the original CLIP embedding for better customization to the cooking understanding problems: 1) from the upstream perspective, we extend the static multi-modal CLIPembedding with a temporal dimension, to facilitate context-aware semantic understanding; 2) from the downstream perspective, we introduce the concept of zero-shot embedding to sequence-to-sequence dense prediction domains, facilitating CLIPbeing not only capable of telling “Which” (cross-modal recognition), but also capable of telling “When” (cross-context localization). Experiments conducted on two challenging cooking caption generation benchmarks, YouCook and CrossTask, demonstrate the effectiveness of the proposed embedding.

Modern software systems are increasingly multi-lingual, which consist of components developed in different programming languages to reuse existing libraries and com-bine language features. Foreign function interface (FFI) is a mechanism that enables interoperation between a host language and a guest language. CFFI interoperating with external C is part of the language standard for almost all languages. For example, Python/C API is the CFFI between Python and C/C++. Python host with C guest can achieve both productivity and performance, and is widely used by many mainstream software systems in different application domains. The popularity of these software systems makes high demands on program analysis of multilingual codebases. A fundamental challenge is to construct call graphs that capture the connectivity between host and guest languages. In this work, we present a novel approach to call graph construction for calls from different host languages to C/C++ foreign functions. The semantics of the foreign function declaration interfaces are modeled to establish cross-language call relationships, taking into account the semantic abstraction to support different host languages. Graph transformation and function node fusions are defined to build the complete call graphs. We demonstrate experimentally that static call graphs for Python and JavaScript calling C/C++ can be constructed effectively and automatically. The call graph construction can further efficiently work as an IDE language server and integrate with other tools.

Static type inference is an effective way to maintain the safety of programs written in a dynamically typed language. However, foreign functions implemented in another programming language are often outside the inference range. Python, a popular dynamically typed language, has a lot of widely used packages which follow the multilingual structure with C/C++ extension modules. Existing deterministic Python static type inference tools which are not based on type annotations can do nothing about these foreign functions. In this paper, we propose a novel method to infer the type signature of foreign functions by analyzing implicit information in the layer of foreign function interface. We design a static type inference system, its evaluation on CPython, NumPy and Pillow shows that our method soundly infers the number and type of arguments for most foreign functions. Our results can further work as a complement to the state-of-the-art Python static type inference tool and enable it to analyze programs with foreign function calls. We catch 48 bugs of mismatch between foreign function declaration and its implementation, which make a parameter-free foreign function take argument of any type. 8 of the bugs we reported have been confirmed and fixed by communities.

In a t , n threshold secret sharing (SS) scheme, whether or not a shareholder set is an authorized set totally depends on the number of shareholders in the set. When the access structure is not threshold, (t,n) threshold SS is not suitable. This paper proposes a new kind of SS named grouped secret sharing (GSS), which is specific multipartite SS. Moreover, in order to implement GSS, we utilize both Lagrange interpolation polynomials and Chinese remainder theorem to design two GSS schemes, respectively. Detailed analysis shows that both GSS schemes are correct and perfect, which means any authorized set can recover the secret while an unauthorized set cannot get any information about the secret.

Protein-protein interactions play essential roles in various biological progresses. Identifying protein interaction sites can facilitate researchers to understand life activities and therefore will be helpful for drug design. However, the number of experimental determined protein interaction sites is far less than that of protein sites in protein-protein interaction or protein complexes. Therefore, the negative and positive samples are usually imbalanced, which is common but bring result bias on the prediction of protein interaction sites by computational approaches. In this work, we presented three imbalance data processing strategies to reconstruct the original dataset, and then extracted protein features from the evolutionary conservation of amino acids to build a predictor for identification of protein interaction sites. On a dataset with 10,430 surface residues but only 2,299 interface residues, the imbalance dataset processing strategies can obviously reduce the prediction bias, and therefore improve the prediction performance of protein interaction sites. The experimental results show that our prediction models can achieve a better prediction performance, such as a prediction accuracy of 0.758, or a high F-measure of 0.737, which demonstrated the effectiveness of our method.

Pathogenicity-related studies are of great importance in understanding the pathogenesis of complex diseases and improving the level of clinical medicine. This work proposed a bioinformatics scheme to analyze cancer-related gene mutations, and try to figure out potential genes associated with diseases from the protein domain-domain interaction network. Herein, five measures of the principle of centrality lethality had been adopted to implement potential correlation analysis, and prioritize the significance of genes. This method was further applied to KEGG pathway analysis by taking the malignant melanoma as an example. The experimental results show that 25 domains can be found, and 18 of them have high potential to be pathogenically important related to malignant melanoma. Finally, a web-based tool, named Human Cancer Related Domain Interaction Network Analyzer, is developed for potential pathogenic genes prioritization for 26 types of human cancers, and the analysis results can be visualized and downloaded online.

Android users are now suffering severe threats from unwanted behaviors of various apps. The analysis of apps' audit logs is one of the essential methods for the security analysts of various companies to unveil the underlying maliciousness within apps. We propose and implement AppAngio, a novel system that reveals contextual information in Android app behaviors by API-level audit logs. Our goal is to help security analysts understand how the target apps worked and facilitate the identification of the maliciousness within apps. The key module of AppAngio is identifying the path matched with the logs on the app's control-flow graphs (CFGs). The challenge, however, is that the limited-quantity logs may incur high computational complexity in the log matching, where there are a large number of candidates caused by the coupling relation of successive logs. To address the challenge, we propose a divide and conquer strategy that precisely positions the nodes matched with log records on the corresponding CFGs and connects the nodes with as few backtracks as possible. Our experiments show that AppAngio reveals contextual information of behaviors in real-world apps. Moreover, the revealed results assist the analysts in identifying the maliciousness of app behaviors and complement existing analysis schemes. Meanwhile, AppAngio incurs negligible performance overhead on the real device in the experiments.

This paper focuses on constructions of MDS self-dual codes from (extended) generalized Reed-Solomon (GRS) codes. Let$q = r^{2}$be an odd prime power. We show that, there exists a$q$-ary self-dual (extended) GRS code for each even length in the range$[{2r,3r-3}]$, and for each singly even length in the range$[3r-1,4r]$. This extends the only known consecutive range$[2,2r]$to$[{2,3r-3}]$for this case. Furthermore, our general constructions provide many MDS self-dual codes with new parameters which, to the best of our knowledge, were not reported before.

Querying average distances is useful for real-world applications such as business decision and medical diagnosis, as it can help a decision maker to better understand the users’ data in a database. However, privacy has been an increasing concern. People are now suffering serious privacy leakage from various kinds of sources, especially service providers who provide insufficient protection on user’s private data. In this paper, we discover a new type of attack in an average-distance query (AVGD query) with noisy results. The attack is general that it can be used to reveal private data of different dimensions. We theoretically analyze how different factors affect the accuracy of the attack and propose the privacy-preserving mechanism based on the analysis. We experiment on two real-life datasets to show the feasibility and severity of the attack. The results show that the severity of the attack is mainly influenced by the factors including the noise magnitude, the number of queries, and the number of users in each query. Also, we validate the correctness of our theoretical analysis by comparing with the experimental results and confirm the effectiveness of the privacy-preserving mechanism.

BACKGROUND: The recognition of protein interaction sites is of great significance in many biological processes, signaling pathways and drug designs. However, most sites on protein sequences cannot be defined as interface or non-interface sites because only a small part of protein interactions had been identified, which will cause the lack of prediction accuracy and generalization ability of predictors in protein interaction sites prediction. Therefore, it is necessary to effectively improve prediction performance of protein interaction sites using large amounts of unlabeled data together with small amounts of labeled data and background knowledge today. RESULTS: In this work, three semi-supervised support vector machine-based methods are proposed to improve the performance in the protein interaction sites prediction, in which the information of unlabeled protein sites can be involved. Herein, five features related with the evolutionary conservation of amino acids are extracted from HSSP database and Consurf Sever, i.e., residue spatial sequence spectrum, residue sequence information entropy and relative entropy, residue sequence conserved weight and residual Base evolution rate, to represent the residues within the protein sequence. Then three predictors are built for identifying the interface residues from protein surface using three types of semi-supervised support vector machine algorithms. CONCLUSION: The experimental results demonstrated that the semi-supervised approaches can effectively improve prediction performance of protein interaction sites when unlabeled information is involved into the predictors and one of them can achieve the best prediction performance, i.e., the accuracy of 70.7%, the sensitivity of 62.67% and the specificity of 78.72%, respectively. With comparison to the existing studies, the semi-supervised models show the improvement of the predication performance.

We propose and implement a novel positioning system, WalkieLokie, which directly calculates the relative position from a smart device to a target. The requirement of the target is simple: it is attached with a “dummy” acoustic speaker, which does not have any other rich capabilities, such as audio recording, communication, or computation. Hence, the proliferation of smart devices, together with the cheap accessory (e.g., dummy speaker) embedded in daily used items (e.g., smart clothes), paves the way for WalkieLokie applications. WalkieLokie leverages the walking motion for locating an acoustic speaker. The key insight is that the distance between the user and the speaker varies in real time when the user walks, and the pattern of the variance implies the relative position. We design a novel algorithm to estimate the position and signal processing methods to support accurate positioning. The experiment results show that the mean errors of ranging and direction estimation are 0.63 m and 2.46°, respectively. Extensive experiments conducted in noisy environments validate the robustness of WalkieLokie.

In this paper, we propose and implement WalkieLokie, a novel acoustic-based relative positioning system. WalkieLokie facilitates a multitude of Augmented Reality (AR) applications: users with smart devices can passively acquire surrounding information in real time, similar to the commercial AR system Wikitude; the surrounding presenters, who want to share information or introduce themselves, can actively launch the function on demand. The key rational of WalkieLokie is that a user can perceive a series of spatial-related acoustic signals emitted from a presenter, which depicts the relation position between the user and the presenter. The proliferation of smart devices, together with the cheap accessory (e.g., dummy speaker) embedded in daily used items (e.g., smart clothes), paves the way for WalkieLokie applications. We design a novel algorithm to estimate the position and signal processing methods to support accurate positioning. The experiment results show that the mean error of ranging and direction estimation is 0.63m and 2.46 degrees respectively. Extensive experiments conducted in noisy environments validate the robustness of WalkieLokie.

In this paper, we propose a novel indoor localization scheme, Lightitude, by exploiting ubiquitous visible lights, which are necessarily and densely deployed in almost all indoor environments. Different from existing positioning systems that exploit special LEDs, ubiquitous visible lights lack fingerprints that can uniquely identify the light source, which results in an ambiguity problem that an RLS may correspond to multiple candidate positions. Moreover, received light strength (RLS) is not only determined by device's position, but also seriously affected by its orientation, which causes great complexity in site-survey. To address these challenges, we first propose and validate a realistic light strength model to avoid the expensive site-survey, then harness user's mobility to generate spatial-related RLS to tackle single RLS's position-ambiguity problem. Experiment results show that Lightitude achieves mean accuracy 1.93m and 2.24m in office (720m2) and library scenario (960m2) respectively.

We propose Magemite, a fine-grained input system that exploits the around device space (ADS) as an expansion of the limited input area. The key insight underlying Magemite is, magnetic sensor integrated in smart devices can sense nearby magnetic field strength. Using a permanent magnet, users could “write” in ADS to communicate with matched devices. Different from previous magnetic-sensing schemes that recognize only coarse-grained gestures, Magemite can recognize user's fine-grained input like characters. However, individual's diverse writing patterns affect the recognition accuracy. To address this challenge, we preprocess the input trajectories and abstract different features of trajectories to uniquely identify user's input, then use these feature vectors to train several pattern recognition models for character recognition. We evaluate Magemite in various scenarios, and experimental results show Magemite can achieve average recognition accuracy over 85%.

A basic (t,n)-secret sharing (SS) scheme allows a secret s to be divided into n shares and shared among n shareholders. In the scheme, any t or more than t shareholders can recover the secret while fewer than t shareholders cannot obtain the secret s. But an adversary without any valid share may obtain the secret if there are over t participants in the secret reconstruction. To address this type of attack, we first introduce the notion of randomized component (RC), which binds a share with all participants and protects the share from being exposed to outside without any computational assumption; at the same time, RCs can be used to reconstruct the secret. As one of the applications of RCs, a (t,m,n)-group oriented SS scheme is proposed to cope with the attack in basic (t,n)-SSs, in which once m (m ≥ t) participants form a tightly couple group by generating RCs, the secret can be recovered only if all m RCs are correct, which requires each participant to have a valid share in advance. Moreover, the scheme can secure the secret without any user authentication or share verification. Analyses show the proposed (t,m,n)-group oriented SS is asymptotically perfect and unconditionally secure. RCs can also be applied to build other schemes in a simple way, such as multi-SS, group authentication, and so on.

We propose an accurate acoustic direction finding scheme, Swadloon, according to the arbitrary pattern of phone shaking in a rough horizontal plane. Swadloon leverages sensors of the smartphone without the requirement of any specialized devices. Our Swadloon design exploits a key observation: the relative displacement and velocity of the phone-shaking movement corresponds to the subtle phase and frequency shift of the Doppler effects experienced in the received acoustic signal by the phone. Swadloon tracks the displacement of smartphone relative to the acoustic direction with the resolution less than 1 millimeter. The direction is then obtained by combining the velocity from the displacement with the one from the inertial sensors. Major challenges in implementing Swadloon are to measure the displacement precisely and to estimate the shaking velocity accurately when the speed of phone-shaking is low and changes arbitrarily. We propose rigorous methods to address these challenges, and apply Swadloon to several case studies: Phone-to-Phone direction finding, indoor localization and tracking. Our extensive experiments show that the mean error of direction finding is around 2.1 degree within the range of 32 m. For indoor localization, the 90-percentile errors are under 0.92 m. For real-time tracking, the errors are within 0.4 m for walks of 51 m.

Coexistence of Wi-Fi and ZigBee in 2.4 GHz ISM band is a long standing and challenging problem. Previous solutions either require modifications of current ZigBee protocols or Wi-Fi re-configurations, which is not feasible in large-scale wireless sensor networks. In this paper, we present WizBee, a coexistence system using single-antenna sink without changing current Wi-Fi and ZigBee design. WizBee is based on an observation that Wi-Fi signal is about 5 to 20 dB stronger than ZigBee signal in symmetric area, which leaves much room for applying interference cancelation technique to mitigate Wi-Fi interference, and extract ZigBee signals. However, we need to cancel the Wi-Fi interference perfectly for residual ZigBee signal decoding, which needs more accurate channel coefficient across data transmissions in spite of cross technology interference. For robust and accurate Wi-Fi decoding, we use soft Viterbi decoding with weighted confidence value over interfered subcarriers. Consequently, our solution uses decoded data for channel coefficient estimation instead of conventional training symbol based methods. The key insight is that, the signal recovery opportunity for cross technology coexistence, lies in multi-domain information, such as power, frequency and coding discrepancies. Using these information properly will improve the coexistence network throughput effectively. We implemented WizBee in USRP/GNURadio software radio platform, and studied the decoding performance of interference cancelation technique. Our extensive evaluations under real wireless conditions show that WizBee improves ZigBee throughput up to 1.9x, with median throughput gain of 1.2x.

In opportunistic channel access, the user needs to make real time decisions on when and which channel to access with uncertainty. Assuming perfect channel statistics, several studies have applied optimal stopping theory to derive control strategy for sequential sensing/probing based opportunistically accessing (s-SPA), exploiting temporary opportunities among multiple channels. Meanwhile, numerous multi-arm bandit (MAB)-based approaches have been proposed for online learning of channel selection in periodical sensing/accessing system, however, these schemes fail to exploit the opportunistic diversity in short term. In this paper, we investigate online learning of optimal control in s-SPA systems, where both statistics learning and temporary opportunity utilization are jointly considered. An effective and efficient online policy, so called IE-OSP, is proposed, which theoretically guarantees system converges to the optimal s -SPA strategy with bounded probability. Experimental results further show that, the regret of IE-OSP is almost in optimal logarithmic increasing rate over time, and is sub-linear with the increasing number of channels. Compared with existing solutions, our proposed algorithm achieves 25 ~ 30% throughput gain in typical scenarios.

We propose an accurate acoustic direction finding scheme, Swadloon, according to the arbitrary pattern of phone shaking in rough horizontal plane. Swadloon tracks the displacement of smartphone relative to the acoustic direction with the resolution less than 1 millimeter. The direction is then obtained by combining the velocity from the displacement with the one from the inertial sensors. Major challenges in implementing Swadloon are to measure the displacement precisely and to estimate the shaking velocity accurately when the speed of phone-shaking is low and changes arbitrarily. We propose rigorous methods to address these challenges, and apply Swadloon to several case studies: Phone-to-Phone direction finding, indoor localization and tracking. Our extensive experiments show that the mean error of direction finding is around 2.1° within the range of 32 m. For indoor localization, the 90-percentile errors are under 0.92 m. For real-time tracking, the errors are within 0.4 m for walks of 51 m.

Compressive Sensing (CS) has been recognized as a promising technique to reduce and balance the transmission cost in wireless sensor networks (WSNs). Existing efforts mainly focus on applying CS to reliable WSNs, namely, each wireless link is 100% reliable. However, our experimental results show that traditional compressive data gathering (CDG) could result in arbitrarily bad recovery performance, when the wireless links are lossy. In this paper, we study the impact of packet loss on compressive data gathering and ways to improve its robustness using sparsest random scheduling (SRS). The key idea of our scheme is to treat each sampling value as one CS measurement, which helps us to reduce the impact of packet loss on the recovery accuracy. Our scheme also outperforms the tradition CDG in reliable WSNs in that our scheme has significantly lowered transmission cost. To achieve this, we present a sparsest measurement matrix where each row has only one nonzero element. More importantly, we propose a representation basis to sparsify the gathering data, and prove that our measurement matrix satisfies the restricted isometric property (RIP) with high probability. Extensive experimental results show our scheme can recover the data accurately with packet loss ratio up to $15\%$, while traditional CDG can hardly recover the data under similar or even better conditions.

Compressive sensing (CS)-based in-network data processing is a promising approach to reduce packet transmission in wireless sensor networks. Existing CS-based data gathering methods require a large number of sensors involved in each CS measurement gathering, leading to the relatively high data transmission cost. In this paper, we propose a sparsest random scheduling for compressive data gathering scheme, which decreases each measurement transmission cost from O(N) to O(log(N)) without increasing the number of CS measurements as well. In our scheme, we present a sparsest measurement matrix, where each row has only one nonzero entry. To satisfy the restricted isometric property, we propose a design method for representation basis, which is properly generated according to the sparsest measurement matrix and sensory data. With extensive experiments over real sensory data of CitySee, we demonstrate that our scheme can recover the real sensory data accurately. Surprisingly, our scheme outperforms the dense measurement matrix with a discrete cosine transformation basis over 5 dB on data recovery quality. Simulation results also show that our scheme reduces almost 10 × energy consumption compared with the dense measurement matrix for CS-based data gathering.

Trusted Platform Module (TPM) is a coprocessor for detecting platform integrity and attesting the integrity to the remote entity. There are two obstacles in the application of TPM: minimizing trusted computing base (TCB) for reducing risk of flaws in TCB, for which a number of convincing solutions have been developed; formal guarantees on each level of TCB, where the formal methods on analyzing the application level have not been well addressed. To the best of our knowledge, there is no general formal framework for developing the TPM-based protocol applications, which not only guarantees the security but also makes it easier for design. In this paper, we make fine-grained refinement on TPM-based security protocols to illustrate our formal solution on the application level by using the Event-B language. First, we modify the classical Dolev-Yao attacker model, which assumes normal entity's compliance with the protocol even without TPM's protection. Thus, the classical security protocols are vulnerable in this modified attacker model. Second, we make stepwise refinement of the security protocol by refining the protocol events and adding security constraints. From the fifth refinement, we make a case study to illustrate the entire refinement and further formally prove the key agreement protocol from DAAODV, the TPM-based routing protocol, under the extended Dolev-Yao attacker model. The refinement provides another way of formal modeling the TPM-based security protocols and a more fine-grained model to satisfy with the rigorous security requirement of applying TPM. Finally, we prove all the proof obligations generated by Rodin, an Eclipse-based IDE for Event-B, to ensure the soundness of our proposal.

Data mining is an important technology for the information society. Due to the limited computation resources of data owners and the prevalence of cloud computing, outsourced data mining is becoming more and more attractive. The privacy and security issues are becoming outstanding recently. Though the existing model of cloud computing consists of multiple data owners, there is little consideration for the collaboration between them. But such collaboration is necessary with the trend of data partition among different entities nowadays. Besides, most of the existing work are based on the semi-honest cloud assumption and can not deal with the malicious cloud situation well. In this paper, we explore the secure and practical outsourced collaborative data mining scheme in cloud computing scenarios. We design a simple framework for it and propose several enhanced frameworks and detailed schemes in an incremental way with stronger security considerations. The final framework utilizes trusted computing technology to design the scheme under the malicious cloud assumption. Finally, we give a summary of security and efficiency analysis about them. As a case of study, we prove the correctness of the frameworks with three classical methods KNN, K-means and SVM respectively in such outsourced collaborative computing scenario.

With the rapid development of MANET, the secure and practical authentication problem in it increasingly becomes outstanding. The existing work study the problem from two aspects, i.e. secure key division/distributed storage and secure distributed authentication. But existing cheating problems and fault attack possibility will break the security. Besides, efficiency performance of such schemes is not good enough due to the exponential arithmetic with Shamir's scheme. Due to these problems above, we explore the property of verifiable secret sharing(VSS) schemes with Chinese Remainder Theorem(CRT). Then a secret key distributed storage scheme based on CRT-VSS and trusted computing is proposed for MANET. We utilize trusted computing technology to solve two existing cheating problems in secret sharing area before. After that we do some analysis of the homomorphism property with CRT-VSS scheme. Compared with the secure shares-product sharing scheme based on Shamir's scheme, we design the corresponding scheme base on CRT-VSS scheme with better concision and equal security later. On such basis, a distributed Elliptic Curve-Digital Signature Standard signature (ECC-DSS) authentication scheme based on CRT-VSS scheme and trusted computing is proposed. The choice of the trusted authentication node can eliminates the possibility of traditional DoS and fault attack. At last, we do some security analysis towards our schemes proposed above.

Wireless sensor networks are often deployed in harsh environments, where the adversary is able to capture certain sensors. Once a sensor is compromised, the adversary can easily replicate it and deploy several replicas back into the network for further malicious activities. Although a number of protocols have been proposed to tackle such node replication attacks, few of these schemes are suitable for mobile wireless sensor networks. In this paper, we propose two novel mobility-assisted distributed solutions to node replication detection in mobile wireless sensor networks. In both protocols, after receiving the time-location claims, witnesses carry these claims around the network instead of transmitting them. That means data are forwarded only when appropriate witnesses encounter each other. Unary-Time-Location Storage & Exchange (UTLSE) detects the replicas by each of the two encountered witnesses which stores only one time-location claim. Multi-Time-Location Storage & Diffusion (MTLSD), by storing more time-location claims for each tracked node and introducing time-location claims diffusion among witnesses, provides excellent resiliency and sub-optimal detection probability with modest communication overhead. Due to the mobility-assisted property, our protocols do not rely on any specific routing protocol, which makes them suitable for various mobile settings. Our theoretical analysis and simulation results show that our protocols are efficient in terms of detection performance, communication overhead and storage overhead.