Reviewerly/exHarmony huggingface dataset:
Semantic-Aware Privacy-Preserving Online Location Trajectory Data Sharing

Although users can obtain various services by sharing their location information online with location-based service providers, it reveals sensitive information about users. However, existing privacy-preserving techniques in the online scenario suffer from the following shortcomings. First, they model the correlations between the real trajectory and the distorted trajectory as undirected, which makes them unable to accurately quantify the data privacy leakage caused by sharing the distorted trajectory. Second, they are unable to protect semantic privacy, i.e., attackers can obtain the victims&#x2019; visit purpose by using the Point of Interest information without knowing the real location data. Additionally, they fail to balance semantic-aware data utility and privacy protection. To make the case even worse, compared to the offline scenario, sharing trajectory online in real time does not have access to the overall location trajectory. In this paper, we propose a novel semantic-aware privacy-preserving online location trajectory sharing mechanism, called SEmantic-aware Information-Theoretic Privacy (SEITP), to protect both data privacy and semantic privacy while the semantic-aware data utility can be preserved. In particular, we put forward two new metrics of privacy to capture data privacy leakage and semantic privacy leakage, respectively. Besides, to quantify the semantic-aware trajectory data utility, we propose a semantic-aware utility metric. With those metrics, the shortcoming of failing to guarantee the data utility is avoided naturally through structuring a multi-objective optimization problem. Then, we theoretically prove that the new construction can protect both data and semantic privacy. Finally, the experimental evaluations based on the real-world private vehicle trajectory dataset demonstrate that SEITP outperforms existing mechanisms.
A High Performance Multi-Bit-Width Booth Vector Systolic Accelerator for NAS Optimized Deep Learning Neural Networks

Multi-bit-width convolutional neural network (CNN) maintains the balance between network accuracy and hardware efficiency, thus enlightening a promising method for accurate yet energy-efficient edge computing. In this work, we develop state-of-the-art multi-bit-width accelerator for NAS Optimized deep learning neural networks. To efficiently process the multi-bit-width network inferencing, multi-level optimizations have been proposed. Firstly, differential Neural Architecture Search (NAS) method is adopted for the high accuracy multi-bit-width network generation. Secondly, hybrid Booth based multi-bit-width multiply-add-accumulation (MAC) unit is developed for data processing. Thirdly, vector systolic array is proposed for effectively accelerating the matrix multiplications. With vector-style systolic dataflow, both the processing time and logic resources consumption can be reduced when compared with the classical systolic array. Finally, The proposed multi-bit-width CNN acceleration scheme has been practically deployed on FPGA platform of Xilinx ZCU102. Average performance on accelerating the full NAS optimized VGG16 network is 784.2 GOPS, and peek performance of the convolutional layer can reach as high as 871.26 GOPS for INT8, 1676.96 GOPS for INT4, and 2863.29 GOPS for INT2 respectively, which is among the best results in previous CNN accelerator benchmarks.
An Efficient Distributed Kalman Filter Over Sensor Networks With Maximum Correntropy Criterion

We consider the distributed Kalman filtering (DKF) with non-Gaussian noises problem, where each sensor exchanges information between its neighbors with limited communication. Inspired by the ability to capture higher-order statistics of maximum correntropy criterion (MCC) to deal with non-Gaussian noises, we utilizes a matrix weight instead of a scalar obtained by MCC to improve the estimation performance comparing with existing MCC based DKFs. We approximate the centralized estimate by the covariance intersection method, and propose a new MCC based distributed Kalman filter, named CI-DMCKF. The proposed algorithm only needs to communicate once with neighbors in a sampling period, which is more efficient for low bandwidth communication than existing MCC based DKFs. Under the condition of global observability, we show that the consistency, stability and asymptotic unbiasedness properties of proposed CI-DMCKF algorithm. Finally, we experimentally demonstrate the effectiveness of the proposed algorithm on a cooperating target tracking task.
A Scalable Two-Layer Blockchain System for Distributed Multicloud Storage in IIoT

Blockchain has been utilized to manage distributed multicloud storage in the industrial Internet of Things. Existing approaches commonly use trusted third-party servers or middlewares to search data allocation strategies and use blockchain to enhance security. However, finding a fair data allocation strategy is hard when the third-party brokers are manipulated. Moreover, the complex computing in generating blocks reduces efficiency and heavy communication cost in consensus leads to critical challenges to scalability. To address that, this article proposes a scalable two-layer blockchain system for distributed multi-cloud storage (STSM). We design a novel consensus mechanism called proof of storage allocation, which integrates data placement problems into leader selection to achieve fair strategy and high QoS of data storage. We also incorporate asynchronous consensus groups into the consensus process to enhance scalability. Extensive experiments verify that STSM gains high scalability and increases efficiency while achieving high QoS in distributed multicloud data allocation.
An Anonymity Vulnerability in Tor

Privacy is currently one of the most concerned issues in Cyberspace. Tor is the most widely used system in the world for anonymously accessing Internet. However, Tor is known to be vulnerable to end-to-end traffic correlation attacks when an adversary is able to monitor traffic at both communication endpoints. In this paper, we present a set of novel Trapper Attacks that can be used to deanonymize user activities by both AS-level adversaries and Node-level adversaries in a Tor network. First, AS-level adversaries can exploit the occasional failures of censored network to selectively control entry guards of the Tor users. Second, the adversaries can exploit poor reliability of the Tor communication (e.g., natural churn) to compromise the exiting nodes and the anonymous path. Once the adversaries gain control of the routes, they can identify and inspect any traffic entering and leaving the Tor network, consequently, deanonymize a Tor user’s activity in the network. To demonstrate the effectiveness and feasibility of this attacks, we implemented a tool that can launch the proposed Trapper Attacks to automatic reveal communication relationships between a Tor user and its destinations running on a live Tor network. We also present a formal analysis framework to evaluate the integrity of the Tor network. With this framework, we successfully obtained quantitative estimates of Tor’s security vulnerability. The proposed Trapper Attacks are also designed to scale up in real-world Tor networks. Namely, it allows an adversary to perform deanonymization in honey relays effectively, and compromise the anonymity of Tor clients in real time. Our experimental results show that the proposed attacks succeed in less than 40 seconds achieving a 100% accuracy rate and a false positive rate close to 0.
Blockchain-Envisioned Provably Secure Multivariate Identity-Based Multi-Signature Scheme for Internet of Vehicles Environment

The deployed vehicles in an Internet of Vehicles (IoV) can take intelligent decisions by means of exchanging the real-time traffic-related information between the vehicles and IoV infrastructures. This further reduces the probability of the traffic jams and accidents. However, the insecure (public) communication among the various entities in IoV makes various security threats and attacks that can be launched by passive/active adversaries present in the network. In view of this context, there is a need of an efficient cryptographic primitive which can produce single compact signature. A multi-signature scheme (MSS) empowers a collection of signers to conjointly sign a given message using a single compact signature that can be verified by any verifier. Herein, we put forward a new identity-based multivariate MSS, namely <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MV-MSS</monospace> , which is built on top of the intractability of multivariate-quadratic (MQ) problem. The fact is that multivariate public key cryptosystem provides fast, post-quantum safe and efficient primitives, which makes it the front runner candidate among the post-quantum cryptographic candidates. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MV-MSS</monospace> is proven to be secure in the existential unforgeability under chosen-message and chosen identity attack model if solving the MQ problem is NP-hard. We then incorporate the designed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MV-MSS</monospace> in IoV application where the leader (cluster head) selected from a group of vehicles in a dynamic cluster forms the multi-signatures on the messages securely received from its member vehicles. Later, the messages along with their multi-signatures are forwarded to the nearby road-side unit (RSU) of the cluster head, which are then forwarded to a cloud server in the blockchain center maintained by a Peer-to-Peer (P2P) cloud servers network. In this way, the messages and their signatures considered as transactions are put in blocks and added into a public blockchain with the help of consensus algorithm. A comparative study among the proposed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MV-MSS</monospace> and other existing schemes shows that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MV-MSS</monospace> is efficient and secure as compared to other schemes. Finally, a blockchain implementation through simulation study has been performed to show its practical use in IoV application.
CNNPC: End-Edge-Cloud Collaborative CNN Inference With Joint Model Partition and Compression

Edge Intelligence (EI) aims at addressing concerns like response latency risen by the conflict between predominating Cloud-based deployments of computationally intensive AI applications and the expensive uploading of explosive end data. Convolutional Neural Networks (CNNs) leading the latest flourish of AI inevitably suffer from the aforementioned conflict. There emerge increasing EI-driven attempts on fast CNN inference with high accuracy in the End-Edge-Cloud (EEC) collaborative computing paradigm, where, however, neither model compression approaches for on-device inference nor collaborative inference methods across devices can effectively achieve the trade-off between latency and accuracy of End-to-End (E2E) inference. In this article, we present CNNPC that jointly partitions and compresses CNNs for fast inference with high accuracy in collaborative EEC systems. We implemented CNNPC (source code available at <uri>https://github.com/IoTDATALab/CNNPC</uri> ) and evaluated its performance within extensive real-world EEC scenarios. Experimental results demonstrate that, compared with state-of-the-art single-end and collaborative approaches, without obvious accuracy loss, collaborative inference based on CNNPC is up to <inline-formula><tex-math notation="LaTeX">$1.6\times$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$5.6\times$</tex-math></inline-formula> faster, and requires as low as <inline-formula><tex-math notation="LaTeX">$4.30\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$6.48\%$</tex-math></inline-formula> communications, respectively. Besides, when determines the optimal strategy, CNNPC requires as low as <inline-formula><tex-math notation="LaTeX">$0.1\%$</tex-math></inline-formula> actual compression operations that the traversal method (the only viable method providing the theoretically optimal strategy) requires.
Universal Approximation Power of Deep Residual Neural Networks Through the Lens of Control

In this article, we show that deep residual neural networks have the power of universal approximation by using, in an essential manner, the observation that these networks can be modeled as nonlinear control systems. We first study the problem of using a deep residual neural network to exactly memorize training data by formulating it as a controllability problem for an ensemble control system. Using techniques from geometric control theory, we identify a class of activation functions that allow us to ensure controllability on an open and dense submanifold of sample points. Using this result, and resorting to the notion of monotonicity, we establish that any continuous function can be approximated on a compact set to arbitrary accuracy, with respect to the uniform norm, by this class of neural networks. Moreover, we provide optimal bounds on the number of required neurons.
An Efficient and Privacy-Preserving Blockchain-Based Authentication Scheme for Low Earth Orbit Satellite-Assisted Internet of Things

Recently, integrating satellite networks (e.g., low-Earth-orbit (LEO) satellite constellation) into the Internet of Things (IoT) ecosystem has emerged as a potential paradigm to provide more reliable, ubiquitous, and seamless network services. The LEO satellite networks serves as a key enabler to transform the connectivity across industries and geographical border. Despite the convenience brought from the LEO satellite networks, it arises security concerns, in which the essential one is to secure the communication between the IoT devices and the LEO satellite network. However, some challenges inheriting from the LEO satellite networks need to be considered, which are: the dynamic topology; the resource-constraint satellites; the relative long latency; and multiple beams authentication. In particular, the centralized authentication schemes are no longer suitable for the emerging LEO satellite-assisted IoT ecosystem. In this article, we first introduce the architecture of the LEO satellite network-assisted IoT ecosystem. Then, we propose an efficient and privacy-preserving blockchain-based authentication scheme. The proposed authentication scheme takes the advantages of certificateless encryption and consortium blockchain to provide lightweight key pair computation without appealing devices’ information and efficient signature querying and verification. In addition, a fast authentication mechanism is implemented in the scheme in order to reduce the time complexity from querying a certain record for the authentication within a satellite among multiple beams. With the analysis of the storage and computation complexity, the performance evaluation demonstrates the effectiveness of the proposed scheme.
Incremental Learning and Conditional Drift Adaptation for Nonstationary Industrial Process Fault Diagnosis

Incremental learning-based fault diagnosis is effective to learn from continuous industrial data on an ongoing basis. However, in the case of nonstationary industrial processes, new data distribution often gradually shifts away from that of historical data, due to equipment aging and manufacturing strategies. Thus, conventional incremental methods with the identical independent distribution (i.i.d.) assumption may no longer promise satisfied diagnosis performance. This article concerns the conditional drift phenomenon, a relaxation of the i.i.d. assumption, in which the conditional distribution of industrial data changes within different time. From a mathematical point of view, we first give the problem formulation of conditional drift and introduce a target mapping strategy for drift adaptation, under the minimum risk criteria. Then, following this strategy, an incremental diagnosis model with adaptation ability is designed. Particularly, a transformation matrix keeps matching the distributions of historical and new data. Thus, our method can quickly adapt to the conditional drift and be more robust against evolving environment. The proposed method is applied for diagnosing faults in two industrial processes to demonstrate its effectiveness.
Automated Risk Management Based Software Security Vulnerabilities Management

An automated risk assessment approach is explored in this work. The focus is to optimize the conventional threat modeling approach to explore software system vulnerabilities. Data produced in the software development processes are better leveraged using Machine Learning approaches. A large amount of industry knowledge around security vulnerabilities can be leveraged to enhance current threat modeling approaches. Work done here is in the ecosystem of software development processes that use Agile methodology. Insurance business domain data are explored as a target for this study. The focus is to enhance the traditional threat modeling approach with a better quantitative approach and reduce the biases introduced by the people who are part of software development processes. This effort will help bridge multiple data sources prevalent across the software development ecosystem. Bringing these various data sources together will assist in understanding patterns associated with security aspects of the software systems. This perspective further helps to understand and devise better controls. Approaches explored so far have considered individual areas of software development and their influence on improving security. There is a need to build an integrated approach for a total security solution for the software systems. A wide variety of machine learning approaches and ensemble approaches will be explored. The insurance business domain is considered for the research here. CWE (Common Weaknesses Enumeration) mapping from industry knowledge are leveraged to validate the security needs from the industry perspective. This combination of industry and company data will help get a holistic picture of the software system&#x2019;s security. Combining the industry and company data helps lay down the path for an integrated security management system in software development. The risk management framework with the quantitative threat modeling process is the work&#x2019;s uniqueness. This work contributes towards making the software systems secure and robust with time.
Adaptive Control of Local Updating and Model Compression for Efficient Federated Learning

Data generated at the network edge can be processed locally by leveraging the paradigm of Edge Computing (EC). Aided by EC, Federated Learning (FL) has been becoming a practical and popular approach for distributed machine learning over locally distributed data. However, FL faces three critical challenges, i.e., resource constraint, system heterogeneity and context dynamics in EC. To address these challenges, we present a training-efficient FL method, termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedLamp</i> , by optimizing both the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> ocal upd <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</b> ting frequency and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</b> odel com <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</b> ression ratio in the resource-constrained EC systems. We theoretically analyze the model convergence rate and obtain a convergence upper bound related to the local updating frequency and model compression ratio. Upon the convergence bound, we propose a control algorithm, that adaptively determines diverse and appropriate local updating frequencies and model compression ratios for different edge nodes, so as to reduce the waiting time and enhance the training efficiency. We evaluate the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedLamp</i> through extensive simulation and testbed experiments. Evaluation results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedLamp</i> can reduce the traffic consumption by 63% and the completion time by about 52% for achieving the similar test accuracy, compared to the baselines.
Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge

Neural Architecture Search (NAS) is quickly becoming the go-to approach to optimize the structure of Deep Learning (DL) models for complex tasks such as Image Classification or Object Detection. However, many other relevant applications of DL, especially at the edge, are based on time-series processing and require models with unique features, for which NAS is less explored. This work focuses in particular on Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing that has recently emerged as a promising alternative to more complex recurrent architectures. We propose the first NAS tool that explicitly targets the optimization of the most peculiar architectural parameters of TCNs, namely dilation, receptive-field and number of features in each layer. The proposed approach searches for networks that offer good trade-offs between accuracy and number of parameters/operations, enabling an efficient deployment on embedded platforms. We test the proposed NAS on four real-world, edge-relevant tasks, involving audio and bio-signals. Results show that, starting from a single seed network, our method is capable of obtaining a rich collection of Pareto optimal architectures, among which we obtain models with the same accuracy as the seed, and 15.9-152x fewer parameters. Compared to three state-of-the-art NAS tools, ProxylessNAS, MorphNet and FBNetV2, our method explores a larger search space for TCNs (up to 10^12x) and obtains superior solutions, while requiring low GPU memory and search time. We deploy our NAS outputs on two distinct edge devices, the multicore GreenWaves Technology GAP8 IoT processor and the single-core STMicroelectronics STM32H7 microcontroller. With respect to the state-of-the-art hand-tuned models, we reduce latency and energy of up to 5.5x and 3.8x on the two targets respectively, without any accuracy loss.