Biblio

Deep learning has made remarkable achievements in various domains. Active learning, which aims to reduce the budget for training a machine-learning model, is especially useful for the Deep learning tasks with the demand of a large number of labeled samples. Unfortunately, our empirical study finds that many of the active learning heuristics are not effective when applied to Deep learning models in batch settings. To tackle these limitations, we propose a density weighted diversity based query strategy (DWDS), which makes use of the geometry of the samples. Within a limited labeling budget, DWDS enhances model performance by querying labels for the new training samples with the maximum informativeness and representativeness. Furthermore, we propose a beam-search based method to obtain a good approximation to the optimum of such samples. Our experiments show that DWDS outperforms existing algorithms in Deep learning tasks.

The security issue of complex network systems, such as communication systems and power grids, has attracted increasing attention due to cascading failure threats. Many existing studies have investigated the robustness of complex networks against cascading failure from an attacker's perspective. However, most of them focus on the synchronous attack in which the network components under attack are removed synchronously rather than in a sequential fashion. Most recent pioneering work on sequential attack designs the attack strategies based on simple heuristics like degree and load information, which may ignore the inside functions of nodes. In the paper, we exploit a reinforcement learning-based sequential attack method to investigate the impact of different nodes on cascading failure. Besides, a candidate pool strategy is proposed to improve the performance of the reinforcement learning method. Simulation results on Barabási-Albert scale-free networks and real-world networks have demonstrated the superiority and effectiveness of the proposed method.

The design of attacks for cyber physical systems is critical to assess CPS resilience at design time and run-time, and to generate rich datasets from testbeds for research. Attacks against cyber physical systems distinguish themselves from IT attacks in that the main objective is to harm the physical system. Therefore, both cyber and physical system knowledge are needed to design such attacks. The current practice to generate attacks either focuses on the cyber part of the system using IT cyber security existing body of knowledge, or uses heuristics to inject attacks that could potentially harm the physical process. In this paper, we present a systematic approach to automatically generate integrity attacks from the CPS safety and control specifications, without knowledge of the physical system or its dynamics. The generated attacks violate the system operational and safety requirements, hence present a genuine test for system resilience. We present an algorithm to automate the malware payload development. Several examples are given throughout the paper to illustrate the proposed approach.

Today's companies are increasingly relying on Internet of Everything (IoE) to modernize their operations. The very complexes characteristics of such system expose their applications and their exchanged data to multiples risks and security breaches that make them targets for cyber attacks. The aim of our work in this paper is to provide an cybersecurity strategy whose objective is to prevent and anticipate threats related to the IoE. An economic approach is used in order to help to take decisions according to the reduction of the risks generated by the non definition of the appropriate levels of security. The considered problem have been resolved by exploiting a combinatorial optimization approach with a practical case of knapsack. We opted for a bi-objective modeling under uncertainty with a constraint of cardinality and a given budget to be respected. To guarantee a robustness of our strategy, we have also considered the criterion of uncertainty by taking into account all the possible threats that can be generated by a cyber attacks over IoE. Our strategy have been implemented and simulated under MATLAB environement and its performance results have been compared to those obtained by NSGA-II metaheuristic. Our proposed cyber security strategy recorded a clear improvment of efficiency according to the optimization of the security level and cost parametrs.

In this paper we propose a security and cost aware scheduling heuristic for real-time workflow jobs that process Internet of Things (IoT) data with various security requirements. The environment under study is a four-tier architecture, consisting of IoT, mist, fog and cloud layers. The resources in the mist, fog and cloud tiers are considered to be heterogeneous. The proposed scheduling approach is compared to a baseline strategy, which is security aware, but not cost aware. The performance evaluation of both heuristics is conducted via simulation, under different values of security level probabilities for the initial IoT input data of the entry tasks of the workflow jobs.

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.

The signcryption technique was first proposed by Y. Zheng, where two cryptographic operations digital signature and message encryption are made combinedly. We cryptanalyze the technique and observe that the signature and encryption become vulnerable if the forged public keys are used. This paper proposes an improvement using modified DSS (Digital Signature Standard) version of ElGamal signature and DHP (Diffie-Hellman key exchange protocol), and shows that the vulnerabilities in both the signature and encryption methods used in Zheng's signcryption are circumvented. DHP is used for session symmetric key establishment and it is combined with the signature in such a way that the vulnerabilities of DHP can be avoided. The security and performance analysis of our signcryption technique are provided and found that our scheme is secure and designed using minimum possible operations with comparable computation cost of Zheng's scheme.

With the rapid development of Internet of Things technology and sensor networks, large amount of data is facing security challenges in the transmission process. In the process of data transmission, the standardization and authentication of data sources are very important. A digital signature scheme based on bilinear pairing problem is designed. In this scheme, by signing the authorization mechanism, the management node can control the signature process and distribute data. The use of private key segmentation mechanism can reduce the performance requirements of sensor nodes. The reasonable combination of timestamp mechanism can ensure the time limit of signature and be verified after the data is sent. It is hoped that the implementation of this scheme can improve the security of data transmission on the Internet of things environment.

In this paper, we extend the existing classification of signature models by Cao. To do so, we present a new signature classification framework and migrate the original classification to build an easily extendable faceted signature classification. We propose 20 new properties, 7 property families, and 1 signature classification type. With our classification, theoretically, up to 11 541 420 signature classes can be built, which should cover almost all existing signature schemes.

Although many digital signature algorithms are available nowadays, the speed of signing and/or verifying a digital signature is crucial for different applications. Some algorithms are fast for signing but slow for verification, but others are the inverse. Research efforts for an algorithm being fast in both signing and verification is essential. The traditional GOST algorithm has the shortest signing time but longest verification time compared with other DSA algorithms. Hence an improvement in its signature verification time is sought in this work. A modified GOST digital signature algorithm variant is developed improve the signature verification speed by reducing the computation complexity as well as benefiting from its efficient signing speed. The obtained signature verification execution speed for this variant was 1.5 time faster than that for the original algorithm. Obviously, all parameters' values used, such as public and private key, random numbers, etc. for both signing and verification processes were the same. Hence, this algorithm variant will prove suitable for applications that require short time for both, signing and verification processes. Keywords— Discrete Algorithms, Authentication, Digital Signature Algorithms DSA, GOST, Data Integrity

In cyberspace, a digital signature is a mathematical technique that plays a significant role, especially in validating the authenticity of digital messages, emails, or documents. Furthermore, the digital signature mechanism allows the recipient to trust the authenticity of the received message that is coming from the said sender and that the message was not altered in transit. Moreover, a digital signature provides a solution to the problems of tampering and impersonation in digital communications. In a real-life example, it is equivalent to a handwritten signature or stamp seal, but it offers more security. This paper proposes a scheme to enable users to digitally sign their communications by validating their identity through users’ mobile devices. This is done by utilizing the user’s ambient Wi-Fi-enabled devices. Moreover, the proposed scheme depends on something that a user possesses (i.e., Wi-Fi-enabled devices), and something that is in the user’s environment (i.e., ambient Wi-Fi access points) where the validation process is implemented, in a way that requires no effort from users and removes the "weak link" from the validation process. The proposed scheme was experimentally examined.

With the emergence of quantum computers, traditional digital signature schemes based on problems such as large integer solutions and discrete logarithms will no longer be secure, and it is urgent to find effective digital signature schemes that can resist quantum attacks. Lattice cryptography has the advantages of computational simplicity and high security. In this paper, we propose an identity-based digital signature scheme based on the rejection sampling algorithm. Unlike most schemes that use a common Gaussian distribution, this paper uses a bimodal Gaussian distribution, which improves efficiency. The identity-based signature scheme is more convenient for practical application than the traditional certificate-based signature scheme.

An oblivious signature is a digital signature with some property. The oblivious signature scheme has two parties, the signer and the receiver. First, the receiver can choose one and get one of n valid signatures without knowing the signer’s private key. Second, the signer does not know which signature is chosen by the receiver. In this paper, we propose the oblivious signature which is combined with blind signature and zero-knowledge set membership. The property of blind signature makes sure that the signer does not know the message of the signature by the receiver chosen, on the other hand, the property of the zero-knowledge set membership makes sure that the message of the signature by the receiver chosen is one of the set original messages.

Recently, federated learning (FL), as an advanced and practical solution, has been applied to deal with privacy-preserving issues in distributed multi-party federated modeling. However, most existing FL methods focus on the same privacy-preserving budget while ignoring various privacy requirements of participants. In this paper, we for the first time propose an algorithm (PLU-FedOA) to optimize the deep neural network of horizontal FL with personalized local differential privacy. For such considerations, we design two approaches: PLU, which allows clients to upload local updates under differential privacy-preserving of personally selected privacy level, and FedOA, which helps the server aggregates local parameters with optimized weight in mixed privacy-preserving scenarios. Moreover, we theoretically analyze the effect on privacy and optimization of our approaches. Finally, we verify PLU-FedOA on real-world datasets.

Keystroke dynamics is a behavioural biometric form of authentication based on the inherent typing behaviour of an individual. While this technique is gaining traction, protecting the privacy of the users is of utmost importance. Fully Homomorphic Encryption is a technique that allows performing computation on encrypted data, which enables processing of sensitive data in an untrusted environment. FHE is also known to be “future-proof” since it is a lattice-based cryptosystem that is regarded as quantum-safe. It has seen significant performance improvements over the years with substantially increased developer-friendly tools. We propose a neural network for keystroke analysis trained using differential privacy to speed up training while preserving privacy and predicting on encrypted data using FHE to keep the users' privacy intact while offering sufficient usability.

Federated learning (FL) allows to train a massive amount of data privately due to its decentralized structure. Stochastic gradient descent (SGD) is commonly used for FL due to its good empirical performance, but sensitive user information can still be inferred from weight updates shared during FL iterations. We consider Gaussian mechanisms to preserve local differential privacy (LDP) of user data in the FL model with SGD. The trade-offs between user privacy, global utility, and transmission rate are proved by defining appropriate metrics for FL with LDP. Compared to existing results, the query sensitivity used in LDP is defined as a variable, and a tighter privacy accounting method is applied. The proposed utility bound allows heterogeneous parameters over all users. Our bounds characterize how much utility decreases and transmission rate increases if a stronger privacy regime is targeted. Furthermore, given a target privacy level, our results guarantee a significantly larger utility and a smaller transmission rate as compared to existing privacy accounting methods.

With the development of network, network security has become a topic of increasing concern. Recent years, machine learning technology has become an effective means of network intrusion detection. However, machine learning technology requires a large amount of data for training, and training data often contains privacy information, which brings a great risk of privacy leakage. At present, there are few researches on data privacy protection in the field of intrusion detection. Regarding the issue of privacy and security, we combine differential privacy and machine learning algorithms, including One-class Support Vector Machine (OCSVM) and Local Outlier Factor(LOF), to propose an hybrid intrusion detection system (IDS) with privacy protection. We add Laplacian noise to the original network intrusion detection data set to get differential privacy data sets with different privacy budgets, and proposed a hybrid IDS model based on machine learning to verify their utility. Experiments show that while protecting data privacy, the hybrid IDS can achieve detection accuracy comparable to traditional machine learning algorithms.

Pedestrian dead reckoning (PDR) is a widely used approach to estimate locations and trajectories. Accessing location-based services with trajectory data can bring convenience to people, but may also raise privacy concerns that need to be addressed. In this paper, a privacy-preserving pedestrian dead reckoning framework is proposed to protect a user’s trajectory privacy based on differential privacy. We introduce two metrics to quantify trajectory privacy and data utility. Our proposed privacy-preserving trajectory extraction algorithm consists of three mechanisms for the initial locations, stride lengths and directions. In addition, we design an adversary model based on particle filtering to evaluate the performance and demonstrate the effectiveness of our proposed framework with our collected sensor reading dataset.

Differential privacy is a concept to quantity the disclosure of private information that is controlled by the privacy parameter ε. However, an intuitive interpretation of ε is needed to explain the privacy loss to data engineers and data subjects. In this paper, we conduct a worst-case study of differential privacy risks. We generalize an existing model and reduce complexity to provide more understandable statements on the privacy loss. To this end, we analyze the impact of parameters and introduce the notion of a global privacy risk and global privacy leak.

Current implementations of Differential Privacy (DP) focus primarily on the privacy of the data release. The planned thesis will investigate steps towards a user-centric approach of DP in the scope of the Internet-of-Things (IoT) which focuses on data subjects, IoT developers, and data analysts. We will conduct user studies to find out more about the often conflicting interests of the involved parties and the encountered challenges. Furthermore, a technical solution will be developed to assist data subjects and analysts in making better informed decisions. As a result, we expect our contributions to be a step towards the development of usable DP for IoT sensor data.

With the rapid development of 5G, the Internet of Things (IoT) and edge computing technologies dramatically improve smart industries' efficiency, such as healthcare, smart agriculture, and smart city. IoT is a data-driven system in which many smart devices generate and collect a massive amount of user privacy data, which may be used to improve users' efficiency. However, these data tend to leak personal privacy when people send it to the Internet. Differential privacy (DP) provides a method for measuring privacy protection and a more flexible privacy protection algorithm. In this paper, we study an estimation problem and propose a new frequency estimation algorithm named MFEA that redesigns the publish process. The algorithm maps a finite data set to an integer range through a hash function, then initializes the data vector according to the mapped value and adds noise through the randomized response. The frequency of all interference data is estimated with maximum likelihood. Compared with the current traditional frequency estimation, our approach achieves better algorithm complexity and error control while satisfying differential privacy protection (LDP).

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.

Noise has been used as a way of protecting privacy of users in public datasets for many decades now. Differential privacy is a new standard to add noise, so that user privacy is protected. When this technique is applied for a single end user data, it's called local differential privacy. In this study, we evaluate the effects of adding noise to generate randomized responses on machine learning models. We generate randomized responses using Gaussian, Laplacian noise on singular end user data as well as correlated end user data. Finally, we provide results that we have observed on a few data sets for various machine learning use cases.

With the recognition of cyberspace as an operating domain, concerted effort is now being placed on addressing it in the whole-of-domain manner found in land, sea, undersea, air, and space domains. Among the first steps in this effort is applying the standard supporting concepts of security, defense, and deterrence to the cyber domain. This paper presents an architecture that helps realize forward defense in cyberspace, wherein adversarial actions are repulsed as close to the origin as possible. However, substantial work remains in making the architecture an operational reality including furthering fundamental research cyber science, conducting design trade-off analysis, and developing appropriate public policy frameworks.

Fog computing is a new computing paradigm that utilizes numerous mutually cooperating terminal devices or network edge devices to provide computing, storage, and communication services. Fog computing extends cloud computing services to the edge of the network, making up for the deficiencies of cloud computing in terms of location awareness, mobility support and latency. However, fog nodes are not active enough to perform tasks, and fog nodes recruited by cloud service providers cannot provide stable and continuous resources, which limits the development of fog computing. In the process of cloud service providers using the resources in the fog nodes to provide services to users, the cloud service providers and fog nodes are selfish and committed to maximizing their own payoffs. This situation makes it easy for the fog node to work negatively during the execution of the task. Limited by the low quality of resource provided by fog nodes, the payoff of cloud service providers has been severely affected. In response to this problem, an appropriate incentive mechanism needs to be established in the fog computing environment to solve the core problems faced by both cloud service providers and fog nodes in maximizing their respective utility, in order to achieve the incentive effect. Therefore, this paper proposes an incentive model based on repeated game, and designs a trigger strategy with credible threats, and obtains the conditions for incentive consistency. Under this condition, the fog node will be forced by the deterrence of the trigger strategy to voluntarily choose the strategy of actively executing the task, so as to avoid the loss of subsequent rewards when it is found to perform the task passively. Then, using evolutionary game theory to analyze the stability of the trigger strategy, it proves the dynamic validity of the incentive consistency condition.