Modern vehicles are equipped with multiple Electronic Control Units (ECUs) communicating over in-vehicle networks such as Controller Area Network (CAN). Inherent security limitations in CAN necessitate the use of Intrusion Detection Systems (IDSs) for protection against potential threats. While some IDSs leverage advanced deep learning to improve accuracy, issues such as long processing time and large memory size remain. Existing Binarized Neural Network (BNN)-based IDSs, proposed as a solution for efficiency, often compromise on accuracy. To this end, we introduce a novel Binarized Convolutional Neural Network (BCNN)-based IDS, designed to exploit the temporal and spatial characteristics of CAN messages to achieve both efficiency and detection accuracy. In particular, our approach includes a novel input generator capturing temporal and spatial correlations of messages, aiding model learning and ensuring high-accuracy performance. Experimental results suggest our IDS effectively reduces memory utilization and detection latency while maintaining high detection rates. Our IDS runs 4 times faster and utilizes only 3.3% of the memory space required by a full-precision CNN-based IDS. Meanwhile, our proposed system demonstrates a detection accuracy between 94.19% and 96.82% relative to the CNN-based IDS across different attack scenarios. This performance marks a noteworthy improvement over existing state-of-the-art BNN-based IDS designs.

In this article, our primary focus is on the current lack of privacy-preserving queries tailored to real-number fields rather than integers. We take multisource private data counting on real numbers (R-PDC) within IoT architecture as a breakthrough point to enable diverse query services without revealing sensitive data. The advantage of this is that the parameters required for queries remain stable and minimal in the scenario of wide numerical domain and dynamic changed data set. First, we present a general R-PDC method based on curve approximation, in which an approximate query function (AQF) is established to approximate the constructed ideal counting curve for element queries on target set. We also demonstrate curve construction process of AQF and provide the feasibility theorem of AQF for counting a target element within an allowable error. By integrating the R-PDC method with fixed-point fully homomorphic encryption, an efficient R-PDC scheme is presented to perform multiparty collaborative queries in IoT. In security aspect, the R-PDC scheme on$(m,\epsilon)$-AQF is proved to be statistically secure against chosen element attack (CEA) for cumulative error$\epsilon $and data set size$m$. Moreover, the scheme achieves$O(nm\gamma)$computation and$O(n^{2}m\gamma)$communication complexities for$n$servers and$\gamma $-length fraction. Finally, as an extension of AQF over single attribute, multidimensional R-PDC method is applied into privacy-preserving Naive Bayes classifier and Apriori algorithm over multiple attributes. Our work provides substantial support and insights for the advancement of privacy computation.

To face urgent concern of privacy leakage on mobile crowdsourcing, some Lattice-Based Cryptographic (LBC) schemes have been applied to the cloud-fog-edge data sharing platform for privacy protection. As an important factor of LBC’s security, current key generation usually involves Preimage Gaussian Sampling for Lattice Trapdoor (PGS-LT) to sample short preimage vector from dual lattice. However, there lacks researches on the implementation of PGS-LT according to entities’ computation and storage capacities. To address this issue, we present a fast double-perturbation scheme that is applied to the cloud-fog-edge data sharing platform. Firstly, we design a fast spherical G-lattice sampling algorithm including two samplers: G-perturbation sampler and G-lattice sampler. Among them, the fast non-spherical G-lattice sampling algorithm is extended to arbitrary bases, and deployed on the G-lattice sampler. Meanwhile, the G-perturbation sampler is designed to sample G-perturbation for converting the non-spherical distribution of output G-lattice vector to the spherical one. Secondly, we optimize the assignment of computational tasks in PGS-LT by considering entities’ abilities in the cloud-fog-edge platform. Moreover, we analyze three types of delegated preimage sampling in terms of Gaussian quality and complexity. The analysis and experimental results show that fast spherical G-lattice sampling provides high Gaussian quality of output vector. Meanwhile, in the aspect of complexity, the G-perturbation sampler has lower time & space complexity than the existing works. The G-lattice sampler remains good performance as it only involves extra integer multiplications in linear time complexity.

In the light of the advantages of ring, more and more Lattice-Based Cryptography (LBC) schemes are designed over it to provide small storage cost and high performance. Gaussian Sampler for Lattice Trapdoor (GSLT) plays an important role for these schemes, especially for key extraction. In this paper, we present an efficient GSLT scheme with On-line and Off-line stages. In the On-line stage, we extend the fast non-spherical Gadget-lattice sampling into the ring setting for high performance, and analyze the covariance matrix of output vectors. Subsequently, two optimized perturbation sampling constructions are designed for non-spherical Gadget-lattice sampler to avoid inefficient Cholesky decomposition during Off-line stage. The first construction aims to the spherical Gaussian distribution of preimage vectors, which is beneficial for theoretical analysis. In contrast, the second one is designed on the non-spherical distribution to improve the efficiency of perturbation sampling without leakage of trapdoor in statistic, and we further provide the method how to choose the Gaussian parameters. The complexity analysis and experimental results show that the On-line stage of our scheme has a better performance in comparison with the other works. In the Off-line stage, both of two perturbation sampling constructions can avoid low efficiency of Cholesky decomposition, and are more suitable for the non-spherical G-lattice sampling. In short, our work provides two candidates on either Gaussian parameter or sampling efficiency, thereby offering more options for key generation in LBC schemes.

Aiming at insufficient situation to express and operate assets in smart contracts, in this article we attempt to add a new asset model into smart contract language (such as SPESC) through combing method of asset's expressions and transactions in real-world contracts. Moreover, a translation mechanism can be set up to accomplish a conversion from the asset model to an executable contract program. On this basis, we propose a new language design toward asset-driven specific smart contracts, called TA-SPESC. This language complies with the structure of real-world contracts and supports a formal definition composed of four modules: Party, asset, term, and contract attribute. This asset model on it can be used to define various types of rights (including the right of ownership, use, possession, usufruct, and disposition of assets), as well as five asset operations (including asset registration, deposit, withdrawal, transfer, and cancellation) to effectively support asset transaction. More important, a series of generation rules are proposed to translate the TA-SPESC contract to an executable contract program. Moreover, taking house rental contract as an example, we provide a TA-SPESC instance and its specific description of translation process according to the generation rules, which supports a semiautomatic generation to executable programs. Finally, the Solidity codes derived from TA-SPESC contracts are run and tested, and the experiment and comparison results indicate that TA-SPESC contracts have high abstraction and low complexity, as well as versatility and convenience of asset transaction, which lead to more reliable software with less errors and fewer misunderstanding.

Though representing a promising approach for personalization, targeting, and recommendation, aggregation of user profiles from multiple social networks will inevitably incur a serious privacy leakage issue. In this paper, we propose a Novel Heterogeneous De-anonymization Scheme (NHDS) aiming at de-anonymizing heterogeneous social networks. NHDS first leverages the network graph structure to significantly reduce the size of candidate set, then exploits user profile information to identify the correct mapping users with a high confidence. Performance evaluation on real-world social network datasets shows that NHDS significantly outperforms the prior schemes. Finally, we perform an empirical study on privacy leakage arising from cross-network aggregation based on four real-world social network datasets. Our findings show that 39.9 percent more information is disclosed through de-anonymization and the de-anonymized ratio is 84 percent. The detailed privacy leakage of user demographics and interests is also examined, which demonstrates the practicality of the identified privacy leakage issue.

This article aims to establish a cryptographic solution to improve security and reliability of the National Institute of Standards and Technology's attribute-based access control (ABAC) model. By breaking down the existing structure of attribute-based encryption, we propose a new cryptographic ABAC (C-ABAC) framework with dynamic policy authorization and real-time attribute credentials. Moreover, a practical C-ABAC construction is proposed to support provable policy decision making and verifiable attribute Tokens among multiple distributed authorities. In this construction, we develop a concrete approach of generating a cryptographic policy from access control markup language. We also prove that attribute Token has existential unforgeability under chosen-attribute and chosen-nonce attacks, and the cryptographic policy is existentially unforgeable under chosen-object attack. In addition, our C-ABAC construction provides semantic security against chosen-plaintext attack with Token and policy queries under the extended general Diffie-Hellman exponent assumption. Finally, we evaluate the performance of the C-ABAC system according to complexity analysis and experimental results. The results show that the C-ABAC system is reliable and easy to implement.

In this paper we address the problem of reliability and security in an open-access data sharing system. We propose a new framework, called cryptographic attribute-based access control (CABAC), in consistent with the standard ABAC model. Moreover, two new mechanisms, real-time Tokens and secure policy decision-making, are introduced for ensuring secure attribute authorization and verifiable policy decision-making. More important, we present a practical CABAC system to support adaptability and flexibility using dynamically chosen policy and real-time attribute acquisition. We prove that our CABAC system is provably secure in four aspects: the attribute Tokens are existentially unforgeable against chosen-time and chosen-attribute attacks, respectively; the secure policy is existentially unforgeable against chosen-object attack under eBDH assumption; and our entire system is semantically secure against chosen-plaintext attack with Token and policy queries under eGDHE assumption.

Privacy inference through meta-data (e.g., IP, Host) analysis of Wi-Fi traffic poses a potentially more serious threat to user privacy. First, it provides a more efficient and scalable approach to infer users' sensitive information without checking the content of Wi-Fi traffic. Second, meta-data based demographics inference can work on both unencrypted and encrypted traffic (e.g., HTTPS traffic). In this study, we present a novel approach to infer user demographic information by exploiting the meta-data of Wi-Fi traffic. We develop an inference framework based on machine learning and evaluate its performance on a real-world dataset, which includes the Wi-Fi access of 28,158 users in five months. The framework extracts four kinds of features from real-world Wi-Fi traffic and applies a novel machine learning technique (XGBoost) to predict user demographics. Our analytical results show that, the overall accuracy of inferring gender and education level of users can be 82 and 78 percent, respectively. It is surprising to show that, even for HTTPS traffic, user demographics can still be predicted at accuracy of 69 and 76 percent, respectively, which well demonstrates the practicality of the proposed privacy inference scheme. Finally, we discuss and evaluate potential mitigation methods for such inference attacks.

The mobile advertisement (ad) network is gaining an increasing interest due to the high popularity of smart phones. Previous researches on the security issues of ad network primarily focus on the privacy, permission and malware detection while less attention has been paid to the traffic consumption issue incurred by ad network. Though it is well known that ad network plays an important role in network consumption, it represents a great challenge of giving a fine-grained classification of ad networks. Inspired by this, different from any previous researches, in this study, we take the initial step towards modeling the network consumption of Ad network in Android. We develop an automatic ad analysis platform to quantify the ad network traffic consumed by android applications (app). To achieve a fine-grained quantification, we combine two sources of network traffic. On one hand, we modify the android webview and log system in system level to capture network traffic accurately. On the other hand, we capture network traffic in router level to collect detailed information of traffic packets, such as packet size and URI. We have evaluated the developed system in terms of normal apps, repacked apps and malicious apps based on the real-world dataset, which is comprised of 93 Android apps. We find out that ad traffic takes major percentage of the whole network traffic caused by Android app. We have also studied the ad library mechanism for 10 popular ad libraries. We found ads from some ad libraries use much more network traffic because they have to be fetched from remote ad libraries each time they are shown to users while other ad libraries allow apps to store ads locally.

Although privacy leaking through content analysis of Wi-Fi traffic has received an increased attention, privacy inference through meta-data (e.g. IP, Host) analysis of Wi-Fi traffic represents a potentially more serious threat to user privacy. Firstly, it represents a more efficient and scalable approach to infer users' sensitive information without checking the content of Wi-Fi traffic. Secondly, meta-data based demographics inference can work on both unencrypted and encrypted traffic (e.g., HTTPS traffic). In this study, we present a novel approach to infer user demographic information by exploiting the meta-data of Wi-Fi traffic. We develop a proof-of-concept prototype, Demographic Information Predictor (DIP) system, and evaluate its performance on a real-world dataset, which includes the Wi-Fi access of 28,158 users in 5 months. DIP extracts four kinds of features from real-world Wi-Fi traffic and proposes a novel machine learning based inference technique to predict user demographics. Our analytical results show that, for unencrypted traffic, DIP can predict gender and education level of users with an accuracy of 78% and 74% respectively. It is surprising to show that, even for HTTPS traffic, user demographics can still be predicted at a precision of 67% and 72% respectively, which well demonstrates the practicality of the proposed privacy inference scheme.

In this paper we present an identity-set-based broadcast encryption scheme with three working modes: positive membership (Select-mode), all member (All-mode), and negative membership (Cut-mode) over the user identity set, simultaneously. The core of our scheme is the implementation of cryptographic representation of subset by using two aggregation functions: Zeros-based aggregation and Poles-based aggregation. These two aggregation functions are capable of compressing any subset into one element in a bilinear map group for determining the membership between an element and a subset. Our scheme achieves the optimal bound of O(1)-size for either ciphertext (consisting of just two elements) or decryption key (one element) for an identity set of large size. We prove that our scheme is secure under the General Diffie-Hellman Exponent (GDHE) assumption.

Malware is a burgeoning threat for smartphones and continuing advancing. Traditional defenses to malware, however, are not suitable for smartphones due to their resource intensive nature. This necessitates the design of novel mechanisms that can consider the specifics of the smartphone malware and smartphones themselves. In this paper, we introduce a lightweight permission enforcement approach-Tap-Wave-Rub (TWR)-for smartphone malware prevention. TWR is based on simple cyber-physical human interactions, i.e., human gestures, that are very quick and intuitive but less likely to be exhibited in users' daily activities. Presence or absence of such gestures, prior to accessing an application, can effectively inform the OS whether the access request is benign or malicious. In particular, we present the design of two mechanisms: 1) acceleration-based phone tapping detection and 2) proximity-based finger tapping, rubbing, or hand waving detection. The first mechanism is geared for near field communication applications, which usually require the user to tap her phone with another device. The second mechanism involves very simple gestures, i.e., tapping or rubbing a finger near the top of phone's screen or waving a hand close to the phone, and broadly appeals to many applications (e.g., SMS). In addition, we present the TWR-enhanced Android permission model, the prototypes implementing the underlying gesture recognition mechanisms, and a variety of novel experiments to evaluate these mechanisms. Our results suggest the proposed approach could be very effective for malware detection/prevention, with quite low false positives and false negatives, while imposing little to no additional burden on the users.

Short-range wireless communication technologies have been used in many security-sensitive smartphone applications and services such as contactless micro payment and device pairing. Typically, the data confidentiality of the existing short-range communication systems relies on so-called “key-exchange then encryption” mechanism, which is inefficient, especially for short communication sessions. In this work, we present${\ssb{PriWhisper}}$—a keyless secure acoustic short-range communication system for smartphones. It is designed to provide a software-based solution to secure smartphone communication without the key agreement phase.${\ssb{PriWhisper}}$adopts the emerging friendly jamming technique from radio communication for data confidentiality. The system prototype is implemented and evaluated on several Android smartphone platforms for efficiency and usability. We theoretically and experimentally analyze the security of our proposed acoustic communication system against eavesdropping. In particular, we study the (in)separability of the data signal and jamming signal against blind signal segmentation (BSS) attacks such as independent component analysis (ICA). The result shows that${\ssb{PriWhisper}}$provides sufficient security guarantees for commercial smartphone applications and yet strong compatibilities with most legacy smartphone platforms. As an application, we also develop${\ssb{AcousAuth}}$—a novel smartphone-empowered system for personal authentication.

ABSTRACT Radio frequency identification (RFID) systems are becoming increasingly ubiquitous in both public and private domains. However, because of the inherent weaknesses of underlying wireless radio communications, RFID systems are plagued with a wide variety of security and privacy threats. A large number of these threats arise because of the tag's promiscuous response to any reader requests. This renders sensitive tag information easily subject to unauthorized reading. Promiscuous tag response also incites different forms of relay attacks whereby a malicious colluding pair, relaying messages between a tag and a reader, can successfully impersonate the tag without actually possessing it. Because of the increasing ubiquity of RFID devices, there is a pressing need for the development of security primitives and protocols to defeat unauthorized reading and relay attacks. However, currently deployed or proposed solutions often fail to satisfy the constraints and requirements of the underlying RFID applications in terms of (one or more of) efficiency, security, and usability. This paper proposes a novel research direction, one that utilizes sensing technologies, to tackle the problems of unauthorized reading and relay attacks with a goal of reconciling the requirements of efficiency, security, and usability. The premise of the proposed work is based on a current technological advancement that enables many RFID tags with low‐cost sensing capabilities. The on‐board tag sensors will be used to acquire useful contextual information about the tag's environment (or its owner, or the tag itself). For defense against unauthorized reading and relay attacks, such context information can be leveraged in two ways. First, contextual information can be used to design context‐aware selective unlocking mechanisms so that tags can selectively respond to reader interrogations and thus minimize the likelihood of unauthorized reading and “ghost‐and‐leech” relay attacks. Second, contextual information can be used as a basis for context‐aware secure transaction verification to defend against special types of relay attacks involving malicious readers. Copyright © 2011 John Wiley & Sons, Ltd.

We introduce a lightweight permission enforcement approach - Tap-Wave-Rub (TWR) - for smartphone malware prevention. TWR is based on simple human gestures (implicit or explicit) that are very quick and intuitive but less likely to be exhibited in users' daily activities. Presence or absence of such gestures, prior to accessing an application, can effectively inform the OS whether the access request is benign or malicious. In this paper, we focus on the design of an accelerometer-based phone tapping detection mechanism. This implicit tapping detection mechanism is geared to prevent malicious access to NFC services, where a user is usually required to tap her phone with another device. We present a variety of novel experiments to evaluate the proposed mechanism. Our results suggest that our approach could be very effective for malware prevention, with quite low false positives and false negatives, while imposing no additional burden on the users. As part of the TWR framework, we also briefly explore explicit gestures (finger tapping, rubbing or hand waving based on proximity sensor), which could be used to protect services which do not have a unique implicit gesture associated with them.

In this paper, we report on a new approach for enhancing security and privacy in certain RFID applications whereby location or location-related information (such as speed) can serve as a legitimate access context. Examples of these applications include access cards, toll cards, credit cards, and other payment tokens. We show that location awareness can be used by both tags and back-end servers for defending against unauthorized reading and relay attacks on RFID systems. On the tag side, we design a location-aware selective unlocking mechanism using which tags can selectively respond to reader interrogations rather than doing so promiscuously. On the server side, we design a location-aware secure transaction verification scheme that allows a bank server to decide whether to approve or deny a payment transaction and detect a specific type of relay attack involving malicious readers. The premise of our work is a current technological advancement that can enable RFID tags with low-cost location (GPS) sensing capabilities. Unlike prior research on this subject, our defenses do not rely on auxiliary devices or require any explicit user involvement.

Even though role-based access control (RBAC) can tremendously help us to minimize the complexity in administering users, it still needs to realize the notion of roles at the resource level. In this paper, we propose a practical cryptographic RBAC model, called role-key hierarchy model, to support various security features, including signature, identification, and encryption on role-key hierarchy. In addition, several advanced features, such as role or user revocation, tracing, and anonymity, are implemented as well. With the help of rich algebraic structure of elliptic curves, we introduce a unified and complete construction of role-based cryptosystem to verify the rationality and validity of our proposed model. In addition, a proof-of-concept prototype implementation and performance evaluation is discussed to demonstrate the feasibility and efficiency of our mechanisms.

Numerous cryptographic techniques have been developed to be used on mobile devices for various security and privacy protections. However, these cryptographic primitives, working under different mathematical assumptions, tend to become more and more complex and intricate, which makes it increasingly more difficult for proper implementation and management. Thus, it is desired to simplify management and improve efficiency by means of designing a general function family to meet a variety of security requirements. In this paper, we present such a family of square functions, including SqHash, SqMAC and SqPRF, based on a specially truncated function (MSB or LSB). We further improve the efficiency of these algorithms by using “circular convolution with carry bits” which makes parallel processing possible. We prove the security of these functions based on the privacy in hidden number problem and hard-core predicate of one-way function. We also show that the proposed schemes achieve better performance with a complexity reduction from O(n2) to O(kn/w) for n-bit message, k-bit output and w-bit word size.

Many RFID tags store valuable information privy to their users that can easily be subject to unauthorized reading, leading to owner tracking or impersonation. RFID tags are also susceptible to different forms of relay attacks. This paper presents novel sensing-enabled defenses to unauthorized reading and relay attacks against RFID systems without necessitating any changes to the traditional RFID usage model. More specifically, the paper proposes the use of on-board tag sensors to (automatically) acquire useful contextual information about the tag's environment (or its owner, or the tag itself). It suggests how this information can be used to achieve two security functionalities. First, such context recognition can be leveraged for the purpose of selective tag unlocking - the tag will respond selectively to reader interrogations, i.e., only when it is deemed safe to do so. Second, context recognition can be used as a basis for transaction verification in order to provide protection against a severe form of relay attacks involving malicious RFID readers. To demonstrate the feasibility of the overall idea, a novel selective unlocking mechanism based on owner's posture recognition is presented. The evaluation of the proposed mechanism shows its effectiveness in significantly raising the bar against many different RFID attacks.

In this paper, we report on a new approach for enhancing security and privacy in certain RFID applications whereby location or location-related information (such as speed) can serve as a legitimate access context. Examples of these applications include access cards, toll cards, credit cards and other payment tokens. We show that location awareness can be used by both tags and back-end servers for defending against unauthorized reading and relay attacks on RFID systems. On the tag side, we design a location-aware selective unlocking mechanism using which tags can selectively respond to reader interrogations rather than doing so promiscuously. On the server side, we design a location-aware secure transaction verification scheme that allows a bank server to decide whether to approve or deny a payment transaction and detect a specific type of relay attack involving malicious readers. The premise of our work is a current technological advancement that can enable RFID tags with low-cost location (GPS) sensing capabilities. Unlike prior research on this subject, our defenses do not rely on auxiliary devices or require any explicit user involvement.

A hybrid cloud is a cloud computing environment in which an organization provides and manages some internal resources and has others provided externally. However, this new environment could bring irretrievable losses to the clients due to a lack of integrity verification mechanism for distributed data outsourcing. To support scalable service and data migration, in this paper we address the construction of a collaborative integrity verification mechanism in hybrid clouds where we consider the existence of multiple cloud service providers to collaboratively store and maintain the clients' data. We propose a collaborative provable data possession scheme adopting the techniques of homomorphic verifiable responses and hash index hierarchy. In addition, we articulate the performance optimization mechanisms for our scheme and prove the security of our scheme based on multi-prover zero-knowledge proof system, which can satisfy the properties of completeness, knowledge soundness, and zero-knowledge. Our experiments also show that our proposed solution only incurs a small constant amount of communications overhead.

Wireless sensor networks (WSNs) appeal to a wide range of applications that involve the monitoring of various physical phenomena. However, WSNs are subject to many threats. In particular, lack of pervasive tamper-resistant hardware results in sensors being easy targets for compromise. Having compromised a sensor, the adversary learns all the sensor secrets, allowing it to later encrypt/decrypt or authenticate messages on behalf of that sensor. This threat is particularly relevant in the novel unattended wireless sensor networks (UWSNs) scenario. UWSNs operate without constant supervision by a trusted sink. UWSN's unattended nature and increased exposure to attacks prompts the need for special techniques geared towards regaining security after being compromised. In this article, we investigate cooperative self-healing in UWSNs and propose various techniques to allow unattended sensors to recover security after compromise. Our techniques provide seamless healing rates even against a very agile and powerful adversary. The effectiveness and viability of our proposed techniques are assessed by thorough analysis and supported by simulation results. Finally, we introduce some real-world issues affecting UWSN deployment and provide some solutions for them as well as a few open problems calling for further investigation.

Abstract Broadcast is a fundamental operation in wireless ad hoc and sensor networks. This paper proposes a simple and efficient broadcast protocol, called Broadcast based on Layered Diffusion (BLD), which is a stateless broadcast protocol with very low message overheads and very low computation requirements. In BLD, nodes do not need to exchange neighborhood information for building a broadcast backbone, but make their rebroadcast decisions locally. The design idea of BLD is to emulate the triangular tessellation for complete area coverage of the network field. It does not require nodes' location information for such tessellation emulation but only exploits the hop count information for each node to make local rebroadcast decision and timing. Simulation results show the effectiveness and efficiency of the BLD protocol and its robustness to localization errors and transmission errors. Copyright © 2009 John Wiley & Sons, Ltd.

Content usage statistics from superdistribution users have great commercial values since they can be used for any number of purposes including marketing, accounting, and/or fraud prevention. However tracking content usage under the superdistribution model poses a great challenge since most content users have no explicit pre-established relationship with the content provider. In this paper, we present our technical design of a content usage tracking scheme in the superdistribution model. We explore the concept of Proof of Data Possession (PDP) to establish a post-relationship between users and the provider so that tracking content is possible. Our tracking scheme is format independent, provides accurate and finer-grained usage tracking while at the same time allows user anonymity and efficient distribution of digital contents to end users. Related issues such as user privacy and fraud reporting are also discussed.

In this paper, we present a cloud framework to provide cloud clustering, workflow scheduling and management, fault tolerance and distributed data storage, data analytics and visualisation services. Using a practical case study, we show that in the process of analyzing multiscale climate data, typical problems plaguing data analysts are faced. These include large datasets and limited computational resources, data complexity and limited knowledge, and varying data structures/formats and the need to integrate different tools. The implementation of our framework to climate studies was a success. This can be seen in its ability to perform spatio-temporal data analysis and visualization of a large multi-dimensional climate dataset with reduced processing time. The framework demonstrates great flexibility and simplicity for end users intending to perform data analysis by aiding the integration of data and tools and enabling interactive visualization on-the-fly. This is coupled with effective utilization of computational resources and data storage systems.

RFID Electronic Toll Collection (ETC) systems have been deployed worldwide to improve toll collection efficiency, reduce road congestion, increase road safety and traveler satisfiability. However, the use of such systems raises a number of security and privacy issues due to unauthorized reading and relay attacks. Unfortunately, currently deployed or proposed solutions targeting these attacks often fail to satisfy the constraints and requirements of the underlying RFID toll road application in terms of (one or more of) efficiency, security, and usability. In this paper, we report our initial work toward a new approach, one that utilizes sensing technologies, to tackle the problems of unauthorized reading and relay attacks in RFID ETC systems by considering efficiency, security, and usability simultaneously. In our approach, on-board tag sensors are used to collect contextual information (location, speed) about the tag. Such contextual information is then used to design context-aware selective unlocking mechanisms for toll cards such that they can selectively respond to reader interrogations and thus minimize the likelihood of unauthorized reading and relay attacks. The premise of our work is a current technological advancement that enables many RFID tags with sensing capabilities.

With recent advances in technology, various wearable sensors have been developed for the monitoring of human physiological parameters. A Body Sensor Network (BSN) consisting of such physiological and biomedical sensor nodes placed on, near or within a human body can be used for real-time health monitoring. In this paper, we describe an on-going effort to develop a system consisting of interconnected BSNs for real-time health monitoring of soldiers. We discuss the background and an application scenario for this project. We describe the preliminary prototype of the system and present a blast source localization application.

Localization is an important research problem in WSNs (wireless sensor networks) and many WSN localization algorithms have been proposed in the literature. For a single sensor node whose anchors' positions are known, the location estimation error lower bound can be computed by using the CRLB (Cramer-Rao Lower Bound). However, it is still unclear to the research community what the localization error lower bound is from a network point of view. In this paper, we derive a lower bound of the expected localization error for a network whose sensor nodes and anchors are randomly distributed according to a Poisson point process. We show that the lower bound of the expected localization error for a network is a function of the anchor density and the variance of anchor-to-sensor distance measurements.

In energy-limited wireless sensor networks, network clustering and sensor scheduling are two efficient techniques for minimizing node energy consumption and maximizing network coverage lifetime. When integrating the two techniques, the challenges are how to select cluster heads and active nodes. In this paper, we propose a coverage-aware clustering protocol. In the proposed protocol, we define a cost metric that favors those nodes being more energy-redundantly covered as better candidates for cluster heads and select active nodes in a way that tries to emulate the most efficient tessellation for area coverage. Our simulation results show that the network coverage lifetime can be significantly improved compared with an existing protocol.

Localization is one of the most important research issues in wireless sensor networks (WSNs). Hop-count based localization has been proposed as a cost-effective alternative to the expensive hardware-based localization schemes. In this paper, we propose a new method to estimate distances between anchors and nodes based on hop-count information. Localization is then achieved using the estimated distances. Simulation results show that the performance of our proposed algorithm is much better than that of the DV-hop algorithm in terms of the node-to- anchor distance estimation and localization accuracy, and the improvement is proportional to the node density.

The need for secure logging is well-understood by the security professionals, including both researchers and practitioners. The ability to efficiently verify all (or some) log entries is important to any application employing secure logging techniques. In this article, we begin by examining the state of the art in secure logging and identify some problems inherent to systems based on trusted third-party servers. We then propose a different approach to secure logging based upon recently developed Forward-Secure Sequential Aggregate (FssAgg) authentication techniques. Our approach offers both space-efficiency and provable security. We illustrate two concrete schemes—one private-verifiable and one public-verifiable—that offer practical secure logging without any reliance on online trusted third parties or secure hardware. We also investigate the concept of immutability in the context of forward-secure sequential aggregate authentication to provide finer grained verification. Finally we evaluate proposed schemes and report on our experience with implementing them within a secure logging system.

The rapid developments in wireless communications, embedded systems and MEMS sensor technologies have boosted the research and development on large-scale wireless sensor networks (WSNs). WSNs have very broad application areas such as security surveillance, environmental monitoring and healthcare monitoring. The paper presents a novel simulation framework called WSN middleware service modules simulation platform (WISDOM). The WISDOM simulator is written in Java and it uses the recursive porous agent simulation toolkit (Repast)O as the simulation engine to perform discrete event-driven simulations. WISDOM was used in WSN research (Wang et al.,2007) to simulate and verify middleware services for routing, sensing activity scheduling, group formation and management, target detection and tracking, and collaborative classification and fusion in a wireless sensor network. The versatility and performance of WISDOM for middleware service protocol development and evaluation have proven to be valuable.

A forward secure sequential aggregate (FssAgg) signature scheme allows a signer to iteratively combine signatures generated in different time intervals - and with different keys - into a single constant-size signature. Such a signature offers forward security, storage/communication efficiency, as well as overall integrity of the signed messages. FssAgg schemes are therefore suitable for data-intensive applications on untrusted and/or unattended devices, e.g., logging systems. The first FssAgg signature scheme [21] is based on bilinear maps and is thus rather costly. In this paper, we propose two more practical FssAgg1 signature schemes. A FssAgg1 signature scheme is a special FssAgg signature scheme where exactly ONE message can be signed at each time interval and key update is invoked immediately after each signature generation. Both new schemes are derived from existing forward secure signature schemes. Unlike the scheme in [21], each new scheme has constant-size public and private keys, constant-size signatures as well as constant-time key update and signature generation complexity. We show how to apply proposed schemes in secure logging applications.

The need for secure logging is well-understood by the security researchers and practitioners. The ability to efficiently verify all (or some) log entries is important to any application employing secure logging techniques. In this paper, we begin by examining the state-of-the-art in secure logging and identify some problems inherent to systems based on trusted third-party servers. We then propose a different approach based upon recently developed Forward-Secure Sequential Aggregate (FssAgg) authentication techniques. Our approach offers both space-efficiency and provable security. We illustrate two concrete schemes – one private-verifiable and one public-verifiable – that offer practical secure logging without any reliance on on-line trusted third parties or secure hardware. We evaluate proposed schemes and report on our experience with implementing them within a secure logging system.

Target tracking is one of the very important applications of WSNs (Wireless Sensor Networks). Traditionally, Kalman filter [1] and its derivatives [2, 3] are some of the most popular algorithms in solving the signal tracking problem. In a WSNs tracking application, the target motion/state update dynamics might be linear or nonlinear depending on the specific scenario. The observation model might vary across the sampling interval. This paper compares the effectiveness, limitations and other related implementation issues in applying Kalman filter and extended Kalman filter to tackle target tracking problem in WSNs.

Unattended Wireless Sensor Networks (UWSNs) are composed of many small resource-constrained devices and operate autonomously, gathering data which is periodically collected by a visiting sink. Unattended mode of operation, deployment in hostile environments and value (or criticality) of collected data are some of the factors that complicate UWSN security. This paper makes two contributions. First, it explores a new threat model involving a mobile adversary who periodically compromises and releases sensors aiming to maximize its advantage and overall knowledge of collected data. Second, it constructs a self-healing protocol that allows sensors to continuously and collectively recover from compromise. The proposed protocol is both effective and efficient, as supported by analytical and simulation results.

Wireless sensors are employed in a wide range of applications. One common feature of many sensor settings is the need to communicate sensed data to some collection point or sink. This communication can be direct (to a mobile collector) or indirect-via other sensors towards a remote sink. In either case, a sensor might not be able to communicate to a sink at will. Instead it might collect data and wait (for a potentially long time) for a signal to upload accumulated data directly. In a hostile setting, a sensor may be compromised and its post-compromise data can be manipulated. One important issue is forward security - how to ensure that pre- compromise data cannot be manipulated? Since a typical sensor is limited in storage and communication facilities, another issue is how to minimize resource consumption by accumulated data. It turns out that current techniques are insufficient to address both challenges. To this end, we explore the notion of forward-secure sequential aggregate (FssAgg) authentication schemes. We consider FssAgg authentication schemes in the contexts of both conventional and public key cryptography and construct a FssAgg MAC scheme and a FssAgg signature scheme, each suitable under different assumptions. This work represents the initial investigation of forward-secure aggregation and, although the proposed schemes are not optimal, we believe it opens a new direction for follow-on research.

JPEG 2000 is an international standard for still image compression in the 21st century. Part 8 of the standard, named JPSEC, is concerned with all the security aspects, in particular to access control and authentication. This paper presents a novel access control scheme for JPEG 2000 image code-streams. The proposed scheme is secure against collusion attacks and highly efficient. The scheme is also very flexible, allowing access control to JPEG 2000 image code-streams according to any combination of resolution, quality layer and region of interest. The "encrypt once, decrypt many ways" property of our scheme is designed to work seamlessly with the "compress once, decompress many ways" feature of the JPEG 2000 image code-streams. Our prototype implementation shows that the scheme is practical and is completely compatible with the core part of the JPEG 2000 standard.

This paper presents an authentication scheme and an access control scheme for JPEG2000 image codestreams based on hash chains. Both schemes are fully compatible with the core part of JPEG2000 standard. The authentication scheme allows users to verify the authenticity of sub-images progressively extracted from a single codestream protected with a single signature, and the access control scheme allows users to access sub-images partially decrypted from a single encrypted JPEG2000 codestream. In addition, the two schemes can be integrated to provide both authenticity and access control simultaneously. Our experiments demonstrate the two scheme are very efficient and highly scalable.