Biblio

Smart meter devices enable a better understanding of the demand at the potential risk of private information leakage. One promising solution to mitigating such risk is to inject noises into the meter data to achieve a certain level of differential privacy. In this paper, we cast one-shot non-intrusive load monitoring (NILM) in the compressive sensing framework, and bridge the gap between theoretical accuracy of NILM inference and differential privacy's parameters. We then derive the valid theoretical bounds to offer insights on how the differential privacy parameters affect the NILM performance. Moreover, we generalize our conclusions by proposing the hierarchical framework to solve the multishot NILM problem. Numerical experiments verify our analytical results and offer better physical insights of differential privacy in various practical scenarios. This also demonstrates the significance of our work for the general privacy preserving mechanism design.

In the era of big data and artificial intelligence, computer networks have become an important infrastructure, and the Internet has become ubiquitous. The most basic property of computer networks is reachability. The needs of the modern Internet mainly include cost, performance, reliability, and security. However, even for experienced network engineers, it is very difficult to manually conFigure the network to meet the needs of the modern large-scale Internet. The engineers often make mistakes, which can cause network paralysis, resulting in incalculable losses. Due to the development of automatic reasoning technology, automatic verification of network configuration is used to avoid mistakes. Network verification is at least an NP-C problem, so it is necessary to compress the network to reduce the network scale, thereby reducing the network verification time. This paper proposes a new model of network modeling, which is more suitable for the verification of network configuration on common protocols (such as RIP, BGP). On the basis of the existing compression method, two compression rules are added to compress the modeled network, reducing network verification time and conducting network reachability verification experiments on common networks. The experimental results are slightly better than the current compression methods.

Honeypots have become an important tool for capturing attacks. Hybrid honeypots, including the front end and the back end, are widely used in research because of the scalability of the front end and the high interactivity of the back end. However, traditional hybrid honeypots have some problems that the flow control is difficult and topology simulation is not realistic. This paper proposes a new architecture based on SDN applied to the hybrid honeypot system for network topology simulation and attack traffic migration. Our system uses the good expansibility and controllability of the SDN controller to simulate a large and realistic network to attract attackers and redirect high-level attacks to a high-interaction honeypot for attack capture and further analysis. It improves the deficiencies in the network spoofing technology and flow control technology in the traditional honeynet. Finally, we set up the experimental environment on the mininet and verified the mechanism. The test results show that the system is more intelligent and the traffic migration is more stealthy.

Digital signature is more appropriate for message security in Wireless Sensors Networks (WSNs), which is energy-limited, than costly encryption. However, it meets with difficulty of verification when a large amount of message-signature pairs swarm into the central node in WSNs. In this paper, a scalable group testing algorithm based on Latin square (SGTLS) is proposed, which focus on both batch verification of signatures and invalid signature identification. To address the problem of long time-delay during individual verification, we adapt aggregate signature for batch verification so as to judge whether there are any invalid signatures among the collection of signatures once. In particular, when batch verification fails, an invalid signature identification algorithm is presented based on scalable OR-checking matrix of Latin square, which can adjust the number of group testing by itself with the variation of invalid signatures. Comprehensive analyses show that SGTLS has more advantages, such as scalability, suitability for parallel computing and flexible design (Latin square is popular), than other algorithm.

In quantum cryptography research area, quantum digital signature is an important research field. To provide a better privacy for users in constructing quantum digital signature, the stronger anonymity of quantum digital signatures is required. Quantum ring signature scheme focuses on anonymity in certain scenarios. Using quantum ring signature scheme, the quantum message signer hides his identity into a group. At the same time, there is no need for any centralized organization when the user uses the quantum ring signature scheme. The group used to hide the signer identity can be immediately selected by the signer himself, and no collaboration between users.Since the quantum finite automaton signature scheme is very efficient quantum digital signature scheme, based on it, we propose a new quantum ring signature scheme. We also showed that the new scheme we proposed is of feasibility, correctness, anonymity, and unforgeability. And furthermore, the new scheme can be implemented only by logical operations, so it is easy to implement.

It is well-known that online services resort to various cookies to track users through users' online service identifiers (IDs) - in other words, when users access online services, various "fingerprints" are left behind in the cyberspace. As they roam around in the physical world while accessing online services via mobile devices, users also leave a series of "footprints" – i.e., hints about their physical locations - in the physical world. This poses a potent new threat to user privacy: one can potentially correlate the "fingerprints" left by the users in the cyberspace with "footprints" left in the physical world to infer and reveal leakage of user physical world privacy, such as frequent user locations or mobility trajectories in the physical world - we refer to this problem as user physical world privacy leakage via user cyberspace privacy leakage. In this paper we address the following fundamental question: what kind - and how much - of user physical world privacy might be leaked if we could get hold of such diverse network datasets even without any physical location information. In order to conduct an in-depth investigation of these questions, we utilize the network data collected via a DPI system at the routers within one of the largest Internet operator in Shanghai, China over a duration of one month. We decompose the fundamental question into the three problems: i) linkage of various online user IDs belonging to the same person via mobility pattern mining; ii) physical location classification via aggregate user mobility patterns over time; and iii) tracking user physical mobility. By developing novel and effective methods for solving each of these problems, we demonstrate that the question of user physical world privacy leakage via user cyberspace privacy leakage is not hypothetical, but indeed poses a real potent threat to user privacy.

Traditionally, network and system configurations are static. Attackers have plenty of time to exploit the system's vulnerabilities and thus they are able to choose when to launch attacks wisely to maximize the damage. An unpredictable system configuration can significantly lift the bar for attackers to conduct successful attacks. Recent years, moving target defense (MTD) has been advocated for this purpose. An MTD mechanism aims to introduce dynamics to the system through changing its configuration continuously over time, which we call adaptations. Though promising, the dynamic system reconfiguration introduces overhead to the applications currently running in the system. It is critical to determine the right time to conduct adaptations and to balance the overhead afforded and the security levels guaranteed. This problem is known as the MTD timing problem. Little prior work has been done to investigate the right time in making adaptations. In this paper, we take the first step to both theoretically and experimentally study the timing problem in moving target defenses. For a broad family of attacks including DDoS attacks and cloud covert channel attacks, we model this problem as a renewal reward process and propose an optimal algorithm in deciding the right time to make adaptations with the objective of minimizing the long-term cost rate. In our experiments, both DDoS attacks and cloud covert channel attacks are studied. Simulations based on real network traffic traces are conducted and we demonstrate that our proposed algorithm outperforms known adaptation schemes.

We propose a time-adaptive sampling method and demonstrate a sampling-time-adaptive single-photon compressive imaging system. In order to achieve self-adapting adjustment of sampling time, the theory of threshold of light intensity estimation accuracy is deduced. According to this threshold, a sampling control module, based on field-programmable gate array, is developed. Finally, the advantage of the time-adaptive sampling method is proved experimentally. Imaging performance experiments show that the time-adaptive sampling method can automatically adjust the sampling time for the change of light intensity of image object to obtain an image with better quality and avoid speculative selection of sampling time.

Smart meters migrate conventional electricity grid into digitally enabled Smart Grid (SG), which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users' demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those “espionage meters". To enjoy the benefits of smart meter measured data without compromising the users' privacy, in this paper, we try to integrate distributed differential privacy (DDP) techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users' energy profiles. Briefly, we add differential private noises to the users' energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users' demand distribution, the utility provider aggregates a given set of historical users' differentially private data, estimates the users' demands, and formulates the data- driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company's real data analysis.

In this paper, differential privacy protection method is applied to matrix factorization method that used to solve the recommendation problem. For centralized recommendation scenarios, a collaborative filtering recommendation model based on matrix factorization is established, and a matrix factorization mechanism satisfying ε-differential privacy is proposed. Firstly, the potential characteristic matrix of users and projects is constructed. Secondly, noise is added to the matrix by the method of target disturbance, which satisfies the differential privacy constraint, then the noise matrix factorization model is obtained. The parameters of the model are obtained by the stochastic gradient descent algorithm. Finally, the differential privacy matrix factorization model is used for score prediction. The effectiveness of the algorithm is evaluated on the public datasets including Movielens and Netflix. The experimental results show that compared with the existing typical recommendation methods, the new matrix factorization method with privacy protection can recommend within a certain range of recommendation accuracy loss while protecting the users' privacy information.

As a clean energy, wind power is massively utilized in net recent years, which significantly reduced the pollution emission created from unit. This article referred to the concept of energy-saving and emission reducing; built a multiple objective function with represent of the emission of CO2& SO2, the coal-fired from units and the lowest unit fees of commitment; Proposed a algorithm to improving NSGA-D (Non-dominated Sorting Genetic Algorithm-II) for the dynamic characteristics, consider of some constraint conditions such as the shortest operation and fault time and climbing etc.; Optimized and commitment discrete magnitude and Load distribution continuous quantity with the double-optimization strategy; Introduced the fuzzy satisfaction-maximizing method to reaching a decision for Pareto solution and also nested into each dynamic solution; Through simulation for 10 units of wind power, the result show that this method is an effective way to optimize the Multi-objective unit commitment modeling in wind power integrated system with Mixed-integer variable.

False data injection attacks (FDIA) on state estimators are a kind of imminent cyber-physical security issue. Fortunately, it has been proved that if a set of measurements is strategically selected and protected, no FDIA will remain undetectable. In this paper, the metric Return on Investment (ROI) is introduced to evaluate the overall returns of the alternative measurement protection schemes (MPS). By setting maximum total ROI as the optimization objective, the previously ignored cost-benefit issue is taken into account to derive a realistic MPS for power utilities. The optimization problem is transformed into the Steiner tree problem in graph theory, where a tree pruning based algorithm is used to reduce the computational complexity and find a quasi-optimal solution with acceptable approximations. The correctness and efficiency of the algorithm are verified by case studies.

In the indoor scenario, visible light communications (VLC) is regarded as one of the most promising candidates for future wireless communications. Recently, the physical layer security for indoor VLC has drawn considerable attention. In this paper, the secrecy capacity of indoor VLC is analyzed. Initially, an VLC system with a transmitter, a legitimate receiver, and an eavesdropper is established. In the system, the nonnegativity, the peak optical intensity constraint and the dimmable average optical intensity constraint are considered. Based on the principle of information theory, the closed-form expressions of the upper and the lower bounds on the secrecy capacity are derived, respectively. Numerical results show that the upper and the lower bounds on secrecy capacity are very tight, which verify the accuracy of the derived closed-form expressions.

The modern power grid, as a critical national infrastructure, is operated as a cyber-physical system. While the Wide-Area Monitoring, Protection and Control Systems (WAMPCS) in the power grid ensures stable dynamical responses by allowing real-time remote control and collecting measurement over across the power grid, they also expose the power grid to potential cyber-attacks. In this paper, we analyze the effects of Time Delay Attacks (TDAs), which disturb stability of the power grid by simply delaying the transfer of measurement and control demands over the grid's cyber infrastructure. Different from the existing work which simulates TDAs' impacts under specific scenarios, we come up with a generic analytical framework to derive the TDAs' effective conditions. In particular, we propose three concepts of TDA margins, TDA boundary, and TDA surface to define the insecure zones where TDAs are able to destabilize the grid. The proposed concepts and analytical results are exemplified in the context of Load Frequency Control (LFC), but can be generalized to other power control applications.

Cloud computing is applied in various domains, among which social recommender systems are well-received because of their effectivity to provide suggestions for users. Social recommender systems perform well in alleviating cold start problem, but it suffers from shilling attack due to its natural openness. Shilling attack is an injection attack mainly acting on the training process of machine learning, which aims to advance or suppress the recommendation ranking of target items. Some researchers have studied the influence of shilling attacks in two perspectives simultaneously, which are user-item's rating and user-user's relation. However, they take more consideration into user-item's rating, and up to now, the construction of user-user's relation has not been explored in depth. To explore shilling attacks with complex relations, in this paper, we propose two novel attack models based on triangle relations in social networks. Furthermore, we explore the influence of these models on five social recommendation algorithms. The experimental results on three datasets show that the recommendation can be affected by the triangle relation attacks. The attack model combined with triangle relation has a better attack effect than the model only based on rating injection and the model combined with random relation. Besides, we compare the functions of triangle relations in friend recommendation and product recommendation.

Although the deep learning technology effectively improves the effect of person re-identification (re-ID) in video surveillance, there is still a lack of efficient framework in practical, especially in terms of computational cost, which usually requires GPU support. So this paper explores to solve the actual running performance and an effective person re-ID framework is proposed. A tiny network is designed for person detection and a triplet network is adopted for training feature extraction network. The motion detection and person detection is combined to speed up the whole process. The proposed framework is tested in practice and the results show that it can run in real-time on an ordinary PC machine. And the accuracy achieves 91.6% in actual data set. It has a good guidance for person re-ID in actual application.

As recently studied, network-on-chip (NoC) suffers growing threats from hardware trojans (HTs), leading to performance degradation or information leakage when it provides communication service in many/multi-core systems. Therefore, defense techniques against NoC HTs experience rapid development in recent years. However, to the best of our knowledge, there are few standard benchmarks developed for the defense techniques evaluation. To address this issue, in this paper, we design a suite of benchmarks which involves multiple NoCs with different HTs, so that researchers can compare various HT defense methods fairly by making use of them. We first briefly introduce the features of target NoC and its infected modules in our benchmarks, and then, detail the design of our NoC HTs in a one-by-one manner. Finally, we evaluate our benchmarks through extensive simulations and report the circuit cost of NoC HTs in terms of area and power consumption, as well as their effects on NoC performance. Besides, comprehensive experiments, including functional testing and side channel analysis are performed to assess the stealthiness of our HTs.

The globalization of supply chain makes semiconductor chips susceptible to various security threats. Design obfuscation techniques have been widely investigated to thwart intellectual property (IP) piracy attacks. Key distribution among IP providers, system integration team, and end users remains as a challenging problem. This work proposes an orthogonal obfuscation method, which utilizes an orthogonal matrix to authenticate obfuscation keys, rather than directly examining each activation key. The proposed method hides the keys by using an orthogonal obfuscation algorithm to increasing the key retrieval time, such that the primary keys for IP cores will not be leaked. The simulation results show that the proposed method reduces the key retrieval time by 36.3% over the baseline. The proposed obfuscation methods have been successfully applied to ISCAS'89 benchmark circuits. Experimental results indicate that the orthogonal obfuscation only increases the area by 3.4% and consumes 4.7% more power than the baseline1.

Affective1 engineering is a methodology of designing products by collecting customer affective needs and translating them into product designs. It usually begins with questionnaire surveys to collect customer affective demands and responses. However, this process is expensive, which can only be conducted periodically in a small scale. With the rapid development of e-commerce, a larger number of customer product reviews are available on the Internet. Many studies have been done using opinion mining and sentiment analysis. However, the existing studies focus on the polarity classification from a single perspective (such as positive and negative). The classification of multiple affective attributes receives less attention. In this paper, 3-class classifications of four different affective attributes (i.e. Soft-Hard, Appealing-Unappealing, Handy-Bulky, and Reliable-Shoddy) are performed by using two classical machine learning algorithms (i.e. Softmax regression and Support Vector Machine) and two deep learning methods (i.e. Restricted Boltzmann machines and Deep Belief Network) on an Amazon dataset. The results show that the accuracy of deep learning methods is above 90%, while the accuracy of classical machine learning methods is about 64%. This indicates that deep learning methods are significantly better than classical machine learning methods.

As the IPv6 protocol has been rapidly developed and applied, the security of IPv6 networks has become the focus of academic and industrial attention. Despite the fact that the IPv6 protocol is designed with security in mind, due to insufficient defense measures of current firewalls and intrusion detection systems for IPv6 networks, the construction of covert channels using fields not defined or reserved in IPv6 protocols may compromise the information systems. By discussing the possibility of constructing storage covert channels within IPv6 protocol fields, 10 types of IPv6 covert channels are constructed with undefined and reserved fields, including the flow label field, the traffic class field of IPv6 header, the reserved fields of IPv6 extension headers and the code field of ICMPv6 header. An IPv6 covert channel detection method based on field matching (CC-Guard) is proposed, and a typical IPv6 network environment is built for testing. In comparison with existing detection tools, the experimental results show that the CC-Guard not only can detect more covert channels consisting of IPv6 extension headers and ICMPv6 headers, but also achieves real-time detection with a lower detection overhead.

Batched network coding is a low-complexity network coding solution to feedbackless multi-hop wireless packet network transmission with packet loss. The data to be transmitted is encoded into batches where each of which consists of a few coded packets. Unlike the traditional forwarding strategy, the intermediate network nodes have to perform recoding, which generates recoded packets by network coding operations restricted within the same batch. Adaptive recoding is a technique to adapt the fluctuation of packet loss by optimizing the number of recoded packets per batch to enhance the throughput. The input rank distribution, which is a piece of information regarding the batches arriving at the node, is required to apply adaptive recoding. However, this distribution is not known in advance in practice as the incoming link's channel condition may change from time to time. On the other hand, to fully utilize the potential of adaptive recoding, we need to have a good estimation of this distribution. In other words, we need to guess this distribution from a few samples so that we can apply adaptive recoding as soon as possible. In this paper, we propose a distributionally robust optimization for adaptive recoding with a small-sample inferred prediction of the input rank distribution. We develop an algorithm to efficiently solve this optimization with the support of theoretical guarantees that our optimization's performance would constitute as a confidence lower bound of the optimal throughput with high probability.

pubcrawl, Network on Chip Security, Scalability, resiliency, resilience, metrics, Tasks on NoC (Network-on-Chip) are less efficient because of long-distance data synchronization. An inefficient task schedule strategy can lead to a large number of remote data accessing that ruins the speedup of parallel execution of multiple tasks. Thus, we propose an energy efficient task schedule to reduce task traffic with a diffusional pattern. The task mapping algorithm can optimize traffic distribution by limit tasks into a small area to reduce NoC activities. Comparing to application-layer optimization, our task mapping can obtain 20% energy saving and 15% latency reduction on average.

Enabled by the industrial Internet, intelligent transportation has made remarkable achievements such as autonomous vehicles by carnegie mellon university (CMU) Navlab, Google Cars, Tesla, etc. Autonomous vehicles benefit, in various aspects, from the cooperation of the industrial Internet and cyber-physical systems. In this process, users in autonomous vehicles submit query contents, such as service interests or user locations, to service providers. However, privacy concerns arise since the query contents are exposed when the users are enjoying the services queried. Existing works on privacy preservation of query contents rely on location perturbation or k-anonymity, and they suffer from insufficient protection of privacy or low query utility incurred by processing multiple queries for a single query content. To achieve sufficient privacy preservation and satisfactory query utility for autonomous vehicles querying services in cyber-physical systems, this article proposes a novel privacy notion of client-based personalized k-anonymity (CPkA). To measure the performance of CPkA, we present a privacy metric and a utility metric, based on which, we formulate two problems to achieve the optimal CPkA in term of privacy and utility. An approach, including two modules, to establish mechanisms which achieve the optimal CPkA is presented. The first module is to build in-group mechanisms for achieving the optimal privacy within each content group. The second module includes linear programming-based methods to compute the optimal grouping strategies. The in-group mechanisms and the grouping strategies are combined to establish optimal CPkA mechanisms, which achieve the optimal privacy or the optimal utility. We employ real-life datasets and synthetic prior distributions to evaluate the CPkA mechanisms established by our approach. The evaluation results illustrate the effectiveness and efficiency of the established mechanisms.

Cloud computing has been widely used because of its low price, high reliability, and generality of services. However, considering that cloud computing transactions between users and service providers are usually asynchronous, data privacy involving users and service providers may lead to a crisis of trust, which in turn hinders the expansion of cloud computing applications. In this paper, we propose DPP, a data privacy-preserving cloud computing scheme based on homomorphic encryption, which achieves correctness, compatibility, and security. DPP implements data privacy-preserving by introducing homomorphic encryption. To verify the security of DPP, we instantiate DPP based on the Paillier homomorphic encryption scheme and evaluate the performance. The experiment results show that the time-consuming of the key steps in the DPP scheme is reasonable and acceptable.

Recent advancement of smart devices and wearable tech-nologies greatly enlarges the variety of personal data people can track. Applications and services can leverage such data to provide better life support, but also impose privacy and security threats. Obfuscation schemes, consequently, have been developed to retain data access while mitigate risks. Compared to offering choices of releasing raw data and not releasing at all, we examine the effect of adding a data obfuscation option on users' disclosure decisions when configuring applications' access, and how that effect varies with data types and application contexts. Our online user experiment shows that users are less likely to block data access when the obfuscation option is available except for locations. This effect significantly differs between applications for domain-specific dynamic tracking data, but not for generic personal traits. We further unpack the role of context and discuss the design opportunities.