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As a result of the rapid development of IoT systems, an increasing number of academics are focusing on finding new applications for IoT systems. For Internet of Things (IoT) systems, crowdsourcing is a prevalent practise. Due to the large number of deployed devices in IoT networks, more research is still required on the privacy and trust issues that arise when utilising crowdsourcing. As a result of the characteristics of social computing, the crowdsourcing network poses issues in terms of confidentiality and reliability. To consolidate and create this industry, we have built a differentially private crowdsourcing system that integrates public and private blockchains to address the privacy and trust issues of conventional crowdsourcing systems. Our proposed solution enables varying levels of privacy protection to protect the user’s identity and location. Moreover, the installation of blockchain networks might potentially ensure the data’s integrity. In the conclusion of this article, the possibility of deploying a crowdsourcing system with blockchain in IoE networks is examined.

Deep generative models have gained much attention given their ability to generate data for applications as varied as healthcare to financial technology to surveillance, and many more - the most popular models being generative adversarial networks and variational auto-encoders. Yet, as with all machine learning models, ever is the concern over security breaches and privacy leaks and deep generative models are no exception. These models have advanced so rapidly in recent years that work on their security is still in its infancy. In an attempt to audit the current and future threats against these models, and to provide a roadmap for defense preparations in the short term, we prepared this comprehensive and specialized survey on the security and privacy preservation of GANs and VAEs. Our focus is on the inner connection between attacks and model architectures and, more specifically, on five components of deep generative models: the training data, the latent code, the generators/decoders of GANs/ VAEs, the discriminators/encoders of GANs/ VAEs, and the generated data. For each model, component and attack, we review the current research progress and identify the key challenges. The paper concludes with a discussion of possible future attacks and research directions in the field., Comment: To be published in IEEE Transactions on Knowledge and Data Engineering

DeepFakes are raising significant social concerns. Although various DeepFake detectors have been developed as forensic countermeasures, these detectors are still vulnerable to attacks. Recently, a few attacks, principally adversarial attacks, have succeeded in cloaking DeepFake images to evade detection. However, these attacks have typical detector-specific designs, which require prior knowledge about the detector, leading to poor transferability. Moreover, these attacks only consider simple security scenarios. Less is known about how effective they are in high-level scenarios where either the detectors or the attacker's knowledge varies. In this paper, we solve the above challenges with presenting a novel detector-agnostic trace removal attack for DeepFake anti-forensics. Instead of investigating the detector side, our attack looks into the original DeepFake creation pipeline, attempting to remove all detectable natural DeepFake traces to render the fake images more "authentic". To implement this attack, first, we perform a DeepFake trace discovery, identifying three discernible traces. Then a trace removal network (TR-Net) is proposed based on an adversarial learning framework involving one generator and multiple discriminators. Each discriminator is responsible for one individual trace representation to avoid cross-trace interference. These discriminators are arranged in parallel, which prompts the generator to remove various traces simultaneously. To evaluate the attack efficacy, we crafted heterogeneous security scenarios where the detectors were embedded with different levels of defense and the attackers' background knowledge of data varies. The experimental results show that the proposed attack can significantly compromise the detection accuracy of six state-of-the-art DeepFake detectors while causing only a negligible loss in visual quality to the original DeepFake samples.

More and more 5G and AI applications demand flexible and low-cost processing of their traffic through diverse virtualized network functions (VNFs) to meet their security and privacy requirements. As such, the Network Function Virtualization (NFV) market has been emerged as a major service market that allows network service providers to trade their network services among customers. Since each service market usually involves complex interplays among players with different roles, efficient mechanisms that guarantee stable and efficient operations of the NFV market are urgently needed. One fundamental problem in the NFV market is how to maximize the social welfare of all players so that all players have incentives to participate in the activities of the market. In this paper, we first formulate a novel social welfare maximization problem in an NFV market of a multi-tier edge cloud network, with the aim to maximize the total revenue collected from all players, and we implement VNF services on Virtual Machines (VMs) leased by service providers to fulfill customers with service requests, where the edge cloud network consists of both cloudlets in edge networks and remote data centers in the core network. We then design an efficient incentive-compatible mechanism for the problem, and analyze the existence of a Nash equilibrium of the mechanism. Also, we consider an online social welfare maximization problem with uncertain values of customers and without the knowledge of future request arrivals, for which we devise an online learning algorithm by adopting the Multi-Armed Bandits (MAB) method with a bounded regret. We finally evaluate the performance of the proposed mechanisms through simulations and a testbed. Results show that the proposed mechanisms deliver up to 27% higher social welfare than those of existing studies

In this paper, we study a novel service reliability augmentation problem in an Edge Computing network, where mobile users request network services with a service function chain (SFC) and reliability expectation requirements. To enhance the service reliability of user requests, it is a common practice to make use of redundant network function (VNF) instance placement in case their primary VNF instances fail. To this end, we first formulate a novel service reliability augmentation problem for each admitted request and show the NP-hardness of the problem. We then provide an admission framework of user requests by placing primary VNF instances of network functions in the SFC to different cloudlets, and then develop a randomized algorithm with a good approximation ratio and high probability for the service reliability augmentation problem, at the expense of moderate resource constraint violations. We also devise a deterministic heuristic for the problem without any resource violation. We thirdly extend the proposed algorithm for a single request to the service reliability augmentation problem for a set of requests. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising and superior to their analytical counterparts.

Networks that constantly transmit information and change structure are becoming increasingly prevalent. However, traditional privacy models are designed to protect static information, such as records in a database or a person's profile information, which seldom changes. This conflict between static models and dynamic environments is dramatically hindering the effectiveness and efficiency of privacy preservation in today's dynamic world. Hence, in this paper, we formally define the concept of dynamic privacy, present two novel perspectives, privacy propagation and accumulation, on the way private information can spread through dynamic cyberspace, and develop associated theories and mechanisms for preserving privacy in advanced complex networks, such as social networking sites where data are constantly being released, shared, and exchanged.

The Internet of Things (IoT) technology provisions unprecedented opportunities to evolve the interconnection among human beings. However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices prevents users from experiencing high efficiency and seamless user experience. To address these shortcomings, the integrated Mobile Edge Computing (MEC) with remote clouds is a promising platform to enable delay-sensitive service provisioning for IoT applications, where edge-clouds (cloudlets) are co-located with wireless access points in the proximity of IoT devices. Thus, computation-intensive and sensing data from IoT devices can be offloaded to the MEC network immediately for processing, and the service response latency can be significantly reduced. In this paper, we first formulate two novel optimization problems for delay-sensitive IoT applications, i.e., the total utility maximization problems under both static and dynamic offloading task request settings, with the aim to maximize the accumulative user satisfaction on the use of the services provided by the MEC, and show the NP-hardness of the defined problems. We then devise efficient approximation and online algorithms with provable performance guarantees for the problems in a special case where the bandwidth capacity constraint is negligible. We also develop efficient heuristic algorithms for the problems with the bandwidth capacity constraint. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising in reducing service delays and enhancing user satisfaction, and the proposed algorithms outperform their counterparts by at least 10.8 percent.

Face recognition can be installed in a surveillance system so that it can be used for monitoring, tracking and access control. An excellent, intelligent surveillance system should be sensitive to the objects far away from the camera. Unfortunately, due to the long-distance, objects like human faces captured by the camera are too small to identify. As to enhance the subtle color differences in the face image, in this paper we first improve the resolution of the captured image using deep convolution neural networks (DCNNs). Then the efficient features are extracted and used to do classification. As for verifying the effectiveness of the proposed method, we used three databases including AR face database, Georgia Tech face database (GT) database, and Labelled Faces in the Wild (LFW) database, altogether, to conduct the training and testing. Compared to the existing approaches, experimental results show that the identification accuracy of the proposed method outperforms any existing approaches.

Advanced persistent threats (APTs) are one of today's major threats to cyber security. Highly determined attackers along with novel and evasive exfiltration techniques mean APT attacks elude most intrusion detection and prevention systems. The result has been significant losses for governments, organizations, and commercial entities. Intriguingly, despite greater efforts to defend against APTs in recent times, frequent upgrades in defense strategies are not leading to increased security and protection. In this paper, we demonstrate this phenomenon in an appropriately designed APT rivalry game that captures the interactions between attackers and defenders. What is shown is that the defender's strategy adjustments actually leave useful information for the attackers, and thus intelligent and rational attackers can improve themselves by analyzing this information. Hence, a critical part of one's defense strategy must be finding a suitable time to adjust one's strategy to ensure attackers learn the least possible information. Another challenge for defenders is determining how to make the best use of one's resources to achieve a satisfactory defense level. In support of these efforts, we figured out the optimal timings of a player's strategy adjustment in terms of information leakage, which form a family of Nash equilibria. Moreover, two learning mechanisms are proposed to help defenders find an appropriate defense level and allocate their resources reasonably. One is based on adversarial bandits, and the other is based on deep reinforcement learning. Experimental simulations show the rationales behind the game and the optimality of the equilibria. The results also demonstrate that players indeed have the ability to improve themselves by learning from past experiences, which shows the necessity of specifying optimal strategy adjustment timings when defending against APTs.

A critical challenge in machine learning is the vulnerability of learning models in defending attacks from malicious adversaries. In this research, we propose game theoretical learning between a variational adversary and a Convolutional Neural Network (CNN), participating in a variable-sum two-player sequential Stackelberg game. Our adversary manipulates the input data distribution to make the CNN misclassify the manipulated data. Our ideal adversarial manipulation is a minimum change to the data which yet is large enough to mislead the CNNs. We propose an optimization procedure to find optimal adversarial manipulations by solving for the Nash equilibrium of the Stackelberg game. Specifically, the adversary's payoff function depends on the data manipulation which is determined by a Variational Autoencoder, while the CNN classifier's payoff functions are evaluated by misclassification errors. The optimization of our adversarial manipulations is defined by Alternating Least Squares and Simulated Annealing. Experimental results demonstrate that our game-theoretic manipulations are able to mislead CNNs that are well trained on the original data as well as on data generated by other models. We then let the CNNs to incorporate our manipulated data which leads to secure classifiers that are empirically the most robust in defending various types of adversarial attacks.

As deep learning models mature, one of the most prescient questions we face is: what is the ideal tradeoff between accuracy, fairness, and privacy (AFP)? Unfortunately, both the privacy and the fairness of a model come at the cost of its accuracy. Hence, an efficient and effective means of fine-tuning the balance between this trinity of needs is critical. Motivated by some curious observations in privacy-accuracy tradeoffs with differentially private stochastic gradient descent (DP-SGD), where fair models sometimes result, we conjecture that fairness might be better managed as an indirect byproduct of this process. Hence, we conduct a series of analyses, both theoretical and empirical, on the impacts of implementing DP-SGD in deep neural network models through gradient clipping and noise addition. The results show that, in deep learning, the number of training epochs is central to striking a balance between AFP because DP-SGD makes the training less stable, providing the possibility of model updates at a low discrimination level without much loss in accuracy. Based on this observation, we designed two different early stopping criteria to help analysts choose the optimal epoch at which to stop training a model so as to achieve their ideal tradeoff. Extensive experiments show that our methods can achieve an ideal balance between AFP.

IEEE In the context of Information Societies, a tremendous amount of information is daily exchanged or released. Among various information-release cases, medical document release has gained significant attention for its potential in improving healthcare service quality and efficacy. However, integrity and origin authentication of released medical documents is the priority in subsequent applications. Moreover, sensitive nature of much of this information also gives rise to a serious privacy threat when medical documents are uncontrollably made available to untrusted third parties. Redactable signatures allow any party to delete pieces of an authenticated document while guaranteeing the origin and integrity authentication of the resulting (released) subdocument. Nevertheless, most of existing redactable signature schemes (RSSs) are vulnerable to dishonest redactors or illegal redaction detection. To address the above issues, we propose two distinct RSSs with flexible release control (RSSs-FRC). We also analyse the performance of our constructions in terms of security, efficiency and functionality. The analysis results show that the performance of our construction has significant advantages over others, from the aspects of security and efficiency.

Privacy leakage becomes increasingly serious because massive volumes of data are constantly shared in diverse booming cyber-physical social networks (CPSN). Differential privacy is one of the dominating privacy-preserving methods, but most of its extensions assume all data users share the same privacy requirement, which fails to satisfy various privacy expectations in practice. To address this issue, customizable privacy preservation based on differential privacy is a potentially promising countermeasure. However, we found that customizable protection will trigger the composition mechanism of differential privacy and leads to unexpected correlations among injected noises that weakens privacy protection and reveal more sensitive inforamtion. As a result, customizable privacy protection is vulnerable to two primary attacks: background knowledge attack and collusion attack. To optimize the tradeoff between customizable privacy preservation and data utility, we propose a customizable reliable differential privacy model (CRDP), which provides customizable protection on each individual while being attack-proof. We define social distance as the shortest path between two nodes, which is used as an index to customize the privacy protection levels, followed by quantitatively modeling the attacks under the framework of differential privacy. We develop a modified Laplacian mechanism in which the noise generation complies with a Markov stochastic process.Consequently, the correlations of noises are properly de-coupled so that CRDP can simultaneously minimize background knowledge attacks and eliminate collusion attacks in this particular scenario. The evaluation results from real-world datasets show the feasibility and superiority of CRDP in terms of customizable privacy preservation and reliable attack resistance.

Ensuring data confidentiality in a vehicular ad hoc network (VANET) is an increasingly important issue. Message confidentiality, user privacy and access control are the most important problems that affect services provided by VANETs. However, access control that addresses data downloads while preserving users' privacy remains an open problem. Based on a set of attributes, the ciphertext-policy attribute-based encryption (CP-ABE) algorithm proposes a party data encryption/decryption mechanism for shared data; consequently, the algorithm has become a popular solution for data-sharing access control. However, the current CP-ABE schemes are still infeasible for VANETs because these schemes use a single authority and inefficient encryption/decryption and ignore revocation mechanisms. Here, over CP-ABE with revocation, we introduce an identity-based scheme that achieves secure data sharing in VANETs. To reduce the computation load for in-vehicle on-board units (OBUs), we outsource computationally intensive encryption and decryption operations to cloud compute nodes. Attributes are decentralized and managed by application service providers that provide services to vehicles based on subscriptions. Comprehensive experimental results and security analysis show that our scheme achieves fine-grained access control while preserving user privacy. Through implementation, performance analysis demonstrates that our scheme is suitable for VANETs.

The cyber-physical social system (CPSS) enables human social interaction from cyberspace to the physical world by sharing an increasing volume of spatial-temporal data. The sensitive information in the shared data is appealing to the adversaries and various attacks. However, most existing research assumes that the privacy protection level is identical regardless of various requirements, which is not practical. Subsequently, this results in either over-protection or failing to resist the leading attacks like collusion attacks. Motivated by this, a personalized model is proposed by using generative adversarial nets (GAN) to achieve differential privacy and thereby enhancing spatial-temporal private data sharing. A Differential Privacy Identifier is added to the classic GAN with a Generator and a Discriminator . The deployment of the differentially Private GAN (P-GAN) enables the generation of the sanitized data that can perfectly approximate the spatial-temporal trajectory while providing high-level privacy protection. P-GAN optimizes the trade-off between personalized privacy protection and improved data utility while maintaining fast convergence. Simultaneously, the random noise generation guaranteed by GAN breaks the correlation of injected noises and make P-GAN attack-proof against the collusion attacks. Extensive evaluation results on real-world datasets show the superiority of P-GAN from the aspects of optimized trade-off and efficiency.

Privacy preserving in machine learning is a crucial issue in industry informatics since data used for training in industries usually contain sensitive information. Existing differentially private machine learning algorithms have not considered the impact of data correlation, which may lead to more privacy leakage than expected in industrial applications. For example, data collected for traffic monitoring may contain some correlated records due to temporal correlation or user correlation. To fill this gap, we propose a correlation reduction scheme with differentially private feature selection considering the issue of privacy loss when data have correlation in machine learning tasks. %The key to the proposed scheme is to describe the data correlation and select features which leads to less data correlation across the whole dataset. The proposed scheme involves five steps with the goal of managing the extent of data correlation, preserving the privacy, and supporting accuracy in the prediction results. In this way, the impact of data correlation is relieved with the proposed feature selection scheme, and moreover, the privacy issue of data correlation in learning is guaranteed. The proposed method can be widely used in machine learning algorithms which provide services in industrial areas. Experiments show that the proposed scheme can produce better prediction results with machine learning tasks and fewer mean square errors for data queries compared to existing schemes., Comment: This paper has been published in IEEE Transactions on Industrial Informatics

Labeling malware or malware clustering is important for identifying new security threats, triaging and building reference datasets. The state-of-the-art Android malware clustering approaches rely heavily on the raw labels from commercial AntiVirus (AV) vendors, which causes misclustering for a substantial number of weakly-labeled malware due to the inconsistent, incomplete and overly generic labels reported by these closed-source AV engines, whose capabilities vary greatly and whose internal mechanisms are opaque (i.e., intermediate detection results are unavailable for clustering). The raw labels are thus often used as the only important source of information for clustering. To address the limitations of the existing approaches, this paper presents Andre, a new ANDroid Hybrid REpresentation Learning approach to clustering weakly-labeled Android malware by preserving heterogeneous information from multiple sources (including the results of static code analysis, the meta-information of an app, and the raw-labels of the AV vendors) to jointly learn a hybrid representation for accurate clustering. The learned representation is then fed into our outlier-aware clustering to partition the weakly-labeled malware into known and unknown families. The malware whose malicious behaviours are close to those of the existing families on the network, are further classified using a three-layer Deep Neural Network (DNN). The unknown malware are clustered using a standard density-based clustering algorithm. We have evaluated our approach using 5,416 ground-truth malware from Drebin and 9,000 malware from VirusShare (uploaded between Mar. 2017 and Feb. 2018), consisting of 3324 weakly-labeled malware. The evaluation shows that Andre effectively clusters weakly-labeled malware which cannot be clustered by the state-of-the-art approaches, while achieving comparable accuracy with those approaches for clustering ground-truth samples.

In multiagent learning, one of the main ways to improve learning performance is to ask for advice from another agent. Contemporary advising methods share a common limitation that a teacher agent can only advise a student agent if the teacher has experience with an identical state. However, in highly complex learning scenarios, such as autonomous driving, it is rare for two agents to experience exactly the same state, which makes the advice less of a learning aid and more of a one-time instruction. In these scenarios, with contemporary methods, agents do not really help each other learn, and the main outcome of their back and forth requests for advice is an exorbitant communications' overhead. In human interactions, teachers are often asked for advice on what to do in situations that students are personally unfamiliar with. In these, we generally draw from similar experiences to formulate advice. This inspired us to provide agents with the same ability when asked for advice on an unfamiliar state. Hence, we propose a model-based self-advising method that allows agents to train a model based on states similar to the state in question to inform its response. As a result, the advice given can not only be used to resolve the current dilemma but also many other similar situations that the student may come across in the future via self-advising. Compared with contemporary methods, our method brings a significant improvement in learning performance with much lower communication overheads.

With the ubiquity of social media, privacy leakage has become a urgent problemfor social media managers. Studying how the privacy information diffuses through social media has attracted much attention. As a prerequisite, modeling privacy information diffusion is important research. Current approaches for modeling information diffusion are not available for privacy information since they did not consider the propagation features of privacy information in social media. Thispaper discusses the problem of modeling privacy information in social media and its challenges. We first analyse the information diffusion paths in the basic parameters of complex network and the high-order structures. We find that the privacy information is different in propagation features and the size of star structures. Second, a new information diffusion model is illustrated to simulate the diffusion process of information in social media by considering the following three parameters: 1) the probability of users receiving this message, 2) the probability that users have a tendency to forward this message and 3) the interest the users hold for this message. Finally, a block mechanism is designed to congest the diffusion of privacy information in social media.

A cyber system may face to multiple attackers from diverse adversaries, who usually employ sophisticated techniques to both continuously steal sensitive data and avoid being detected by defense strategies. This continuous process is typically involved in an advanced persistent threat (APT). Since the game theory is an ideal mathematical model for investigating continuous decision making of competing players, it is broadly used to research the interaction between defenders and APT attackers. Although many researchers are now using the game theory to defend against APT attacks, most of the existing solutions are limited to single-defender, single-attacker scenarios. In the real world, threats by multiple attackers are not uncommon and multiple defenders can be put in place. Therefore, to overcome the limitation of the existing solutions, we develop a multiagent deep reinforcement learning (MADRL) method with a novel sampling approach. The MADRL method allows defenders to create strategies on the fly and share their experience with other defenders. To develop this method, we create a multidefender, multiattacker game model and analyze the equilibrium of this model. The results of a series of experiments demonstrate that, with MADRL, defenders can quickly learn efficient strategies against attackers.

Federated learning is a promising distributed machine learning paradigm that has been playing a significant role in providing privacy-preserving learning solutions. However, alongside all its achievements, there are also limitations. First, traditional frameworks assume that all the clients are voluntary and so will want to participate in training only for improving the model's accuracy. However, in reality, clients usually want to be adequately compensated for the data and resources they will use before participating. Second, today's frameworks do not offer sufficient protection against malicious participants who try to skew a jointly trained model with poisoned updates. To address these concerns, we have developed a more robust federated learning scheme based on joint differential privacy. The framework provides two game-theoretic mechanisms to motivate clients to participate in training. These mechanisms are dominant-strategy truthful, individual rational, and budget-balanced. Further, the influence an adversarial client can have is quantified and restricted, and data privacy is similarly guaranteed in quantitative terms. Experiments with different training models on real-word datasets demonstrate the effectiveness of the proposed approach.

Social media has become a ubiquitous tool for spreading news, messages, and generally allowing for communication between individuals. Hence, studying how our private information might also spread across social media is important research. To date, many studies have used information diffusion models to simulate and then examine how information flows through social networks. But these models are theoretical, and newsworthy information may not behave in the same way as private information, raising the question: Are the observed phenomena indicative of real privacy propagation To explore this question, we assembled a dataset from Twitter comprising propagated information flows for both private and normal information. We then built a graph convolutional network to trace and classify differences in the way each type of information spreads throughout the platform. The results reveal that there are indeed key differences in the diffusion processes of the two types of information. More importantly, we design privacy-preserving methods to reduce the privacy propagation in social media.

© 2014 IEEE. The emergence of the mobile Internet of Things (IoT) has made our lives smarter, relying on its various mobile IoT devices and services provided. However, with the explosively emerging mobile IoT services, malicious attackers can access them in an unauthorized way. In this paper, we designed an Efficient Anonymous User Authentication (E-AUA) protocol between the users and servers based on multiserver architectures, which contain multiple servers to address the problem of network congestion in mobile IoT. Furthermore, the E-AUA protocol was designed with a dual messages mechanism with strong anti-attack ability, lower communication and computation costs. Comparing with the state of the art protocols, our E-AUA protocol reduced both communication and computation costs. We also provided a security analysis to demonstrate that our E-AUA protocol is secure and meets a variety of security requirements in a motivated mobile IoT scenario.

Searching on encrypted data has become a very important technique in cloud computing. Such searches enable the data owner to search on the encrypted data stored on the cloud without leaking any information. To obtain a better search experience, researchers have proposed many schemes which mainly focus on conjunctive and disjunctive keyword searches. However, conjunction of all the keywords may result in very few results, whereas a disjunction will return too many results. With the current schemes, customizing the relevancy of the keywords to obtain the desired results is difficult. To solve these problems, we propose a novel scheme that supports the search with the user-specified number of keywords contained in the search result. This number $n$ can be used to customize the keyword relevancy. As a result, the data owner could obtain the desired search results containing any $n$ keywords of a keyword set. The proposed scheme also supports the traditional disjunctive and conjunctive keyword searches when $n$ equals 1 or the size of the keyword set, respectively. The keyword could be positive or negative. We first formally define its security and then prove that the proposed scheme is secure against the adaptively chosen keyword attack in the standard model and can defend against the offline keyword guessing attack to some extent. Furthermore, we present a theoretical performance comparison with other schemes as well as the experimental performance evaluations on our implemented scheme.

Automated Driving System (ADS) has attracted increasing attention but the state-of-the-art ADS largely depend on vehicle driving parameters and facial features, which lacks reliability. Approaches using physiological based sensors (e.g., electroencephalogram or electrocardiogram) are either too clumsy to wear or impractical to install. In this paper, we propose a novel driver fatigue detection method by embedding surface electromyography (sEMG) sensors on a steering wheel. Compared with the existing methods, our approach is able to collect bio-signals in a non-intrusive way and detect driver fatigue at an earlier stage. The experimental results show that our approach outperforms existing methods with the weighted average F1 scores of about 90%. We also propose promising future directions to deploy this approach in real-life settings, such as applying multimodal learning using several supplementary sensors.

© 2004-2012 IEEE. Accurate malware propagation modeling in wireless ad hoc networks (WANETs) represents a fundamental and open research issue which shows distinguished challenges due to complicated access competition, severe channel interference, and dynamic connectivity. As an effort towards the issue, in this paper, we investigate the malware propagation under two spread schemes including Unicast and Broadcast, in Spread Mode and Communication Mode, respectively. We highlight our contributions in three-fold in the light of previous literature works. First, a bound of malware infection rate for each scheme is provided by applying the wireless network capacity theories. Second, the impact of mobility on malware propagations has been studied. Third, discussion of the relationship between different schemes and practical applications is provided. Numerical simulations and detailed performance analysis show that the Broadcast Scheme with Spread Mode is most dangerous in the sense of malware propagation speed in WANETs, and mobility will greatly increase the risk further. The results achieved in this paper not only provide insights on the malware propagation characteristics in WANETs, but also serve as fundamental guidelines on designing defense schemes.

© 2018 IEEE. Data aggregation plays an important role in the Internet of Things, and its study and analysis has resulted in a range of innovative services and benefits for people. However, the privacy issues associated with raw sensory data raise significant concerns due to the sensitive nature of the user information it often contains. Thus, numerous schemes have been proposed over the last few decades to preserve the privacy of users' data. Most methods are based on encryption technology, which is computationally and communicationally expensive. In addition, most methods can only handle a single aggregation function. Therefore, in this paper, we propose a multifunctional data aggregation method with differential privacy. The method is based on machine learning and can support a wide range of statistical aggregation functions, including additive and non-additive aggregation. It operates within a fog computing architecture, which extends cloud computing to the edge of the network, alleviating much of the computational burden on the cloud server. And, by only reporting the results of the aggregation to the server, communication efficiency is improved. Extensive experimental results show that the proposed method not only answers flexible aggregation queries that meet diversified aggregation goals, but also produces aggregation results with high accuracy.

© 2013 IEEE. This paper surveys the current research status of location privacy issues in mobile applications. The survey spans five aspects of study: the definition of location privacy, attacks and adversaries, mechanisms to preserve the privacy of locations, location privacy metrics, and the current status of location-based applications. Through this comprehensive review, all the interrelated aspects of location privacy are integrated into a unified framework. Additionally, the current research progress in each area is reviewed individually, and the links between existing academic research and its practical applications are identified. This in-depth analysis of the current state-of-play in location privacy is designed to provide a solid foundation for future studies in the field.

© 2004-2012 IEEE. Identifying rumor sources in social networks plays a critical role in limiting the damage caused by them through the timely quarantine of the sources. However, the temporal variation in the topology of social networks and the ongoing dynamic processes challenge our traditional source identification techniques that are considered in static networks. In this paper, we borrow an idea from criminology and propose a novel method to overcome the challenges. First, we reduce the time-varying networks to a series of static networks by introducing a time-integrating window. Second, instead of inspecting every individual in traditional techniques, we adopt a reverse dissemination strategy to specify a set of suspects of the real rumor source. This process addresses the scalability issue of source identification problems, and therefore dramatically promotes the efficiency of rumor source identification. Third, to determine the real source from the suspects, we employ a novel microscopic rumor spreading model to calculate the maximum likelihood (ML) for each suspect. The one who can provide the largest ML estimate is considered as the real source. The evaluations are carried out on real social networks with time-varying topology. The experiment results show that our method can reduce 60 - 90 percent of the source seeking area in various time-varying social networks. The results further indicate that our method can accurately identify the real source, or an individual who is very close to the real source. To the best of our knowledge, the proposed method is the first that can be used to identify rumor sources in time-varying social networks.

© 2013 IEEE. In recent years, the widespread prevalence of smart devices has created a new class of mobile Internet of Thing applications. Called mobile crowdsensing, these techniques use workers with mobile devices to collect data and send it to task requester for rewards. However, to ensure the optimal allocation of tasks, a centralized server needs to know the precise location of each user, but exposing the workers' exact locations raises privacy concerns. In this paper, we propose a data release mechanism for crowdsensing techniques that satisfies differential privacy, providing rigorous protection of worker locations. The partitioning method is based on worker density and considers non-uniform worker distribution. In addition, we propose a geocast region selection method for task assignment that effectively balances the task assignment success rate with worker travel distances and system overheads. Extensive experiments prove that the proposed method not only provides a strict privacy guarantee but also significantly improves performance.

Differential privacy is an essential and prevalent privacy model that has been widely explored in recent decades. This survey provides a comprehensive and structured overview of two research directions: differentially private data publishing and differentially private data analysis. We compare the diverse release mechanisms of differentially private data publishing given a variety of input data in terms of query type, the maximum number of queries, efficiency, and accuracy. We identify two basic frameworks for differentially private data analysis and list the typical algorithms used within each framework. The results are compared and discussed based on output accuracy and efficiency. Further, we propose several possible directions for future research and possible applications.

Sensor networks are a branch of distributed ad hoc networks with a broad range of applications in surveillance and environment monitoring. In these networks, message exchanges are carried out in a multi-hop manner. Due to resource constraints, security professionals often use lightweight protocols, which do not provide adequate security. Even in the absence of constraints, designing a foolproof set of protocols and codes is almost impossible. This leaves the door open to the worms that take advantage of the vulnerabilities to propagate via exploiting the multi-hop message exchange mechanism. This issue has drawn the attention of security researchers recently. In this paper, we investigate the propagation pattern of information in wireless sensor networks based on an extended theory of epidemiology. We develop a geographical susceptible-infective model for this purpose and analytically derive the dynamics of information propagation. Compared with the previous models, ours is more realistic and is distinguished by two key factors that had been neglected before: 1) the proposed model does not purely rely on epidemic theory but rather binds it with geometrical and spatial constraints of real-world sensor networks and 2) it extends to also model the spread dynamics of conflicting information (e.g., a worm and its patch). We do extensive simulations to show the accuracy of our model and compare it with the previous ones. The findings show the common intuition that the infection source is the best location to start patching from, which is not necessarily right. We show that this depends on many factors, including the time it takes for the patch to be developed, worm/patch characteristics as well as the shape of the network.

Wireless body area networks (WBANs), as a promising health-care system, can provide tremendous benefits for timely and continuous patient care and remote health monitoring. Owing to the restriction of communication, computation and power in WBANs, cloud-assisted WBANs, which offer more reliable, intelligent, and timely health-care services for mobile users and patients, are receiving increasing attention. However, how to aggregate the health data multifunctionally and efficiently is still an open issue to the cloud server (CS). In this paper, we propose a privacy-preserving and multifunctional health data aggregation (PPM-HDA) mechanism with fault tolerance for cloud-assisted WBANs. With PPM-HDA, the CS can compute multiple statistical functions of users’ health data in a privacy-preserving way to offer various services. In particular, we first propose a multifunctional health data additive aggregation scheme (MHDA+) to support additive aggregate functions, such as average and variance. Then, we put forward MHDA $^{\oplus }$ as an extension of MHDA+ to support nonadditive aggregations, such as min/max, median, percentile, and histogram. The PPM-HDA can resist differential attacks, which most existing data aggregation schemes suffer from. The security analysis shows that the PPM-HDA can protect users’ privacy against many threats. Performance evaluations illustrate that the computational overhead of MHDA+ is significantly reduced with the assistance of CSs. Our MHDA $^{\oplus }$ scheme is more efficient than previously reported min/max aggregation schemes in terms of communication overhead when the applications require large plaintext space and highly accurate data.

The global diffusion of epidemics, computer viruses, and rumors causes great damage to our society. It is critical to identify the diffusion sources and timely quarantine them. However, most methods proposed so far are unsuitable for diffusion with multiple sources because of the high computational cost and the complex spatiotemporal diffusion processes. In this paper, based on the knowledge of infected nodes and their connections, we propose a novel method to identify multiple diffusion sources, which can address three main issues in this area: 1) how many sources are there? 2) where did the diffusion emerge? and 3) when did the diffusion break out? We first derive an optimization formulation for multi-source identification problem. This is based on altering the original network into a new network concerning two key elements: 1) propagation probability and 2) the number of hops between nodes. Experiments demonstrate that the altered network can accurately reflect the complex diffusion processes with multiple sources. Second, we derive a fast method to optimize the formulation. It has been proved that the proposed method is convergent and the computational complexity is $O(mn~\textrm {log}\alpha)$ , where $\alpha = \alpha (m,n)$ is the slowly growing inverse-Ackermann function, $n$ is the number of infected nodes, and $m$ is the number of edges connecting them. Finally, we introduce an efficient algorithm to estimate the spreading time and the number of diffusion sources. To evaluate the proposed method, we compare the proposed method with many competing methods in various real-world network topologies. Our method shows significant advantages in the estimation of multiple sources and the prediction of spreading time.

Planning is one of the main approaches used to improve agents' working efficiency by making plans beforehand. However, during planning, agents face the risk of having their private information leaked. This paper proposes a novel strong privacy-preserving planning approach for logistic-like problems that are widely adopted as examples in the multi-agent privacy-preserving planning literature. This approach outperforms existing approaches by addressing two challenges: 1) simultaneously achieving strong privacy, completeness and efficiency, and 2) addressing communication constraints. These two challenges are prevalent in many real-world applications including logistics in military environments and packet routing in networks. To tackle these two challenges, our approach adopts the differential privacy technique, which can both guarantee strong privacy and control communication overhead. To the best of our knowledge, this paper is the first to apply differential privacy to the field of multi-agent planning as a means of preserving the privacy of agents for logistic-like problems. We theoretically prove the strong privacy and completeness of our approach and empirically demonstrate its efficiency. We also theoretically analyze the communication overhead of our approach and illustrate how differential privacy can be used to control it.

Cyber attacks are an unfortunate part of society as an increasing amount of critical infrastructure is managed and controlled via the Internet. In order to protect legitimate users, it is critical for us to obtain an accurate and timely understanding of our cyber opponents. However, at the moment we lack effective tools to do this. In this article we summarize the work on modeling malicious activities from various perspectives, discuss the pros and cons of current models, and present promising directions for possible efforts in the near future.

In this paper, we propose a novel zero knowledge grouping proof protocol for RFID Systems. Over the years, several protocols have been proposed in this area but they are either found to be vulnerable to certain attacks or do not comply with the EPC Class $1$ Gen $2$ (C1G2) standard because they use hash functions or other complex encryption schemes. Also, the unique design requirements of grouping proofs have not been fully addressed by many. Our protocol addresses these important security and design gaps in grouping proofs. We present a novel approach based on pseudo random squares and quadratic residuosity to realize a zero knowledge system. Tag operations are limited to functions such as modulo (MOD), exclusive-or (XOR) and $128$ bit Pseudo Random Number Generators (PRNG). These can be easily implemented on passive tags and hence achieves compliance with the EPC Global standard while meeting the security requirements.

The popularity of Twitter attracts more and more spammers. Spammers send unwanted tweets to Twitter users to promote websites or services, which are harmful to normal users. In order to stop spammers, researchers have proposed a number of mechanisms. The focus of recent works is on the application of machine learning techniques into Twitter spam detection. However, tweets are retrieved in a streaming way, and Twitter provides the Streaming API for developers and researchers to access public tweets in real time. There lacks a performance evaluation of existing machine learning-based streaming spam detection methods. In this paper, we bridged the gap by carrying out a performance evaluation, which was from three different aspects of data, feature, and model. A big ground-truth of over 600 million public tweets was created by using a commercial URL-based security tool. For real-time spam detection, we further extracted 12 lightweight features for tweet representation. Spam detection was then transformed to a binary classification problem in the feature space and can be solved by conventional machine learning algorithms. We evaluated the impact of different factors to the spam detection performance, which included spam to nonspam ratio, feature discretization, training data size, data sampling, time-related data, and machine learning algorithms. The results show the streaming spam tweet detection is still a big challenge and a robust detection technique should take into account the three aspects of data, feature, and model.

As a fundamental tool for network management and security, traffic classification has attracted increasing attention in recent years. A significant challenge to the robustness of classification performance comes from zero-day applications previously unknown in traffic classification systems. In this paper, we propose a new scheme of Robust statistical Traffic Classification (RTC) by combining supervised and unsupervised machine learning techniques to meet this challenge. The proposed RTC scheme has the capability of identifying the traffic of zero-day applications as well as accurately discriminating predefined application classes. In addition, we develop a new method for automating the RTC scheme parameters optimization process. The empirical study on real-world traffic data confirms the effectiveness of the proposed scheme. When zero-day applications are present, the classification performance of the new scheme is significantly better than four state-of-the-art methods: random forest, correlation-based classification, semi-supervised clustering, and one-class SVM.

Cropping and random bending are two common attacks in image watermarking. In this paper we propose a novel image-watermarking method to deal with these attacks, as well as other common attacks. In the embedding process, we first preprocess the host image by a Gaussian low-pass filter. Then, a secret key is used to randomly select a number of gray levels and the histogram of the filtered image with respect to these selected gray levels is constructed. After that, a histogram-shape-related index is introduced to choose the pixel groups with the highest number of pixels and a safe band is built between the chosen and nonchosen pixel groups. A watermark-embedding scheme is proposed to insert watermarks into the chosen pixel groups. The usage of the histogram-shape-related index and safe band results in good robustness. Moreover, a novel high-frequency component modification mechanism is also utilized in the embedding scheme to further improve robustness. At the decoding end, based on the available secret key, the watermarked pixel groups are identified and watermarks are extracted from them. The effectiveness of the proposed image-watermarking method is demonstrated by simulation examples.

Online social networks (OSN) have become one of the major platforms for people to exchange information. Both positive information (e.g., ideas, news and opinions) and negative information (e.g., rumors and gossips) spreading in social media can greatly influence our lives. Previously, researchers have proposed models to understand their propagation dynamics. However, those were merely simulations in nature and only focused on the spread of one type of information. Due to the human-related factors involved, simultaneous spread of negative and positive information cannot be thought of the superposition of two independent propagations. In order to fix these deficiencies, we propose an analytical model which is built stochastically from a node level up. It can present the temporal dynamics of spread such as the time people check newly arrived messages or forward them. Moreover, it is capable of capturing people’s behavioral differences in preferring what to believe or disbelieve. We studied the social parameters impact on propagation using this model. We found that some factors such as people’s preference and the injection time of the opposing information are critical to the propagation but some others such as the hearsay forwarding intention have little impact on it. The extensive simulations conducted on the real topologies confirm the high accuracy of our model.

One of the important features of an RFID system is its ability to search for a particular tag among a group of tags. In order to ensure privacy and security of the tags, the search has to be conducted in a secure fashion. To our knowledge not much work has been done in this secure search area of RFID. The minimal work that has been done do not comply with the EPC C1G2 (Class-1 Gen-2) standard since most of them use expensive hash operations or sophisticated encryption schemes that cannot be implemented in the low-cost passive tags that are highly resource constrained. Our work aims to fill this gap by proposing a protocol that is based on simple XOR encryption and 128 bit pseudo random number generators (PRNG), operations that can be easily implemented on low-cost passive tags. Thus, our protocol enables large scale implementations and achieves EPC C1G2 compliance while meeting the security requirements. The protocol provides additional protection by hiding the pseudo-random number during all transmissions using a blind-factor.

Restraining the spread of rumors in online social networks (OSNs) has long been an important but difficult problem to be addressed. Currently, there are mainly two types of methods 1) blocking rumors at the most influential users or community bridges, or 2) spreading truths to clarify the rumors. Each method claims the better performance among all the others according to their own considerations and environments. However, there must be one standing out of the rest. In this paper, we focus on this part of work. The difficulty is that there does not exist a universal standard to evaluate them. In order to address this problem, we carry out a series of empirical and theoretical analysis on the basis of the introduced mathematical model. Based on this mathematical platform, each method will be evaluated by using real OSN data. We have done three types of analysis in this work. First, we compare all the measures of locating important users. The results suggest that the degree and betweenness measures outperform all the others in the Facebook network. Second, we analyze the method of the truth clarification method, and find that this method has a long-term performance while the degree measure performs well only in the early stage. Third, in order to leverage these two methods, we further explore the strategy of different methods working together and their equivalence. Given a fixed budget in the real world, our analysis provides a potential solution to find out a better strategy by integrating both types of methods together. From both the academic and technical perspective, the work in this paper is an important step towards the most practical and optimal strategies of restraining rumors in OSNs.

Statistics-based Internet traffic classification using machine learning techniques has attracted extensive research interest lately, because of the increasing ineffectiveness of traditional port-based and payload-based approaches. In particular, unsupervised learning, that is, traffic clustering, is very important in real-life applications, where labeled training data are difficult to obtain and new patterns keep emerging. Although previous studies have applied some classic clustering algorithms such as K-Means and EM for the task, the quality of resultant traffic clusters was far from satisfactory. In order to improve the accuracy of traffic clustering, we propose a constrained clustering scheme that makes decisions with consideration of some background information in addition to the observed traffic statistics. Specifically, we make use of equivalence set constraints indicating that particular sets of flows are using the same application layer protocols, which can be efficiently inferred from packet headers according to the background knowledge of TCP/IP networking. We model the observed data and constraints using Gaussian mixture density and adapt an approximate algorithm for the maximum likelihood estimation of model parameters. Moreover, we study the effects of unsupervised feature discretization on traffic clustering by using a fundamental binning method. A number of real-world Internet traffic traces have been used in our evaluation, and the results show that the proposed approach not only improves the quality of traffic clusters in terms of overall accuracy and per-class metrics, but also speeds up the convergence.

This paper presents a patchwork-based audio watermarking method to resist de-synchronization attacks such as pitch-scaling, time-scaling, and jitter attacks. At the embedding stage, the watermarks are embedded into the host audio signal in the discrete cosine transform (DCT) domain. Then, a set of synchronization bits are implanted into the watermarked signal in the logarithmic DCT (LDCT) domain. At the decoding stage, we analyze the received audio signal in the LDCT domain to find the scaling factor imposed by an attack. Then, we modify the received signal to remove the scaling effect, together with the embedded synchronization bits. After that, watermarks are extracted from the modified signal. Simulation results show that at the embedding rate of 10 bps, the proposed method achieves 98.9% detection rate on average under the considered de-synchronization attacks. At the embedding rate of 16 bps, it can still obtain 94.7% detection rate on average. So, the proposed method is much more robust to de-synchronization attacks than other patchwork watermarking methods. Compared with the audio watermarking methods designed for tackling de-synchronization attacks, our method has much higher embedding capacity.