Continuous learning constitutes a fundamental capability of artificial intelligence systems, enabling them to incrementally assimilate novel information without succumbing to catastrophic forgetting. Recent research has leveraged Pre-Trained Models (PTMs) to enhance continual learning efficacy. Nevertheless, prevailing methodologies typically depend on a singular pre-trained backbone and freeze all pre-trained parameters to mitigate network forgetting, thereby constraining adaptability to emerging tasks. In this study, we introduce an innovative PTM-based framework featuring a Dual-Representation Backbone Architecture (DRBA), which integrates both invariant and evolved representation networks to concurrently capture static and dynamic features. Building upon DRBA, we propose an Adaptive and Expandable Mixture Model (AEMM) that incrementally incorporates new expert modules with minimal parameter overhead to accommodate the learning of each novel task. To further augment adaptability, we develop a Dynamic Adaptive Representation Fusion Mechanism (DARFM) that processes outputs from both representation networks and autonomously generates data-driven adaptive weights, optimizing the contribution of each representation. This mechanism yields an adaptive, semantically enriched composite representation, thereby maximizing positive knowledge transfer. Additionally, we propose a Dynamic Knowledge Calibration Mechanism (DKCM), comprising prediction and representation calibration processes, to ensure consistency in both predictions and feature representations. This approach achieves a balance between stability and plasticity, even when learning complex datasets. Empirical evaluations substantiate that the proposed approach attains state-of-the-art performance.

Visual object tracking stands out as a crucial foundational task in computer vision that enjoys a long-standing reputation and widespread application. However, in recent years, adversarial attacks on visual trackers have posed significant threats to their robustness. Regrettably, existing defense methods encounter challenges in transferring from siamese trackers to transformer-based trackers, together with the severe performance decline on clean samples. These shortcomings impede the scalability of existing defense methods on heterogeneous trackers, thereby posing challenges to real-world applications. To address these issues, we present AADN++, a more transferable adversarial defense network. Specifically, based on the observation that attacked latent features tend to deviate from original features at different convolutional scales, we introduces the Latent Feature Loss to improve defense performance and transferability. The LFL is comprised of multi-scale feature loss and classification-regression loss. Furthermore, we enhance the adversarial training process by incorporating an extra forward pass to boost tracking accuracy on clean samples. Experimental evaluations on the OTB100, VOT2018, and LaSOT benchmarks demonstrate that AADN++ exhibits superior defense transferability on heterogeneous trackers and exhibits outstanding robustness against generative and iterative attacks.

Plasticity and stability denote the ability to assimilate new tasks while preserving previously acquired knowledge, representing two important concepts in continual learning. Recent research addresses stability by leveraging pre-trained models to provide informative representations, yet the efficacy of these methods is highly reliant on the choice of the pre-trained backbone, which may not yield optimal plasticity. This paper addresses this limitation by introducing a streamlined and potent framework that orchestrates multiple different pre-trained backbones to derive semantically rich multi-source representations. We propose an innovative Multi-Scale Interaction and Dynamic Fusion (MSIDF) technique to process and selectively capture the most relevant parts of multi-source features through a series of learnable attention modules, thereby helping to learn better decision boundaries to boost performance. Furthermore, we introduce a novel Multi-Level Representation Optimization (MLRO) strategy to adaptively refine the representation networks, offering adaptive representations that enhance plasticity. To mitigate over-regularization issues, we propose a novel Adaptive Regularization Optimization (ARO) method to manage and optimize a switch vector that selectively governs the updating process of each representation layer, which promotes the new task learning. The proposed MLRO and ARO approaches are collectively optimized within a unified optimization framework to achieve an optimal trade-off between plasticity and stability. Our extensive experimental evaluations reveal that the proposed framework attains state-of-the-art performance. The source code of our algorithm is available at https://github.com/CL-Coder236/LMSRR.

Continual learning requires the model to continually capture novel information without forgetting prior knowledge. Nonetheless, existing studies predominantly address the catastrophic forgetting, often neglecting enhancements in model robustness. Consequently, these methodologies fall short in real-time applications, such as autonomous driving, where data samples frequently exhibit noise due to environmental and lighting variations, thereby impairing model efficacy and causing safety issues. In this paper, we address robustness in continual learning systems by introducing an innovative approach, the Dynamic Siamese Expansion Framework (DSEF) that employs a Siamese backbone architecture, comprising static and dynamic components, to facilitate the learning of both global and local representations over time. Specifically, the proposed framework dynamically generates a lightweight expert for each novel task, leveraging the Siamese backbone to enable rapid adaptation. A novel Robust Dynamic Representation Optimization (RDRO) approach is proposed to incrementally update the dynamic backbone by maintaining all previously acquired representations and prediction patterns of historical experts, thereby fostering new task learning without inducing detrimental knowledge transfer. Additionally, we propose a novel Robust Feature Fusion (RFF) approach to incrementally amalgamate robust representations from all historical experts into the expert construction process. A novel mutual information-based technique is employed to derive adaptive weights for feature fusion by assessing the knowledge relevance between historical experts and the new task, thus maximizing positive knowledge transfer effects. A comprehensive experimental evaluation, benchmarking our approach against established baselines, demonstrates that our method achieves state-of-the-art performance even under adversarial attacks.

Extant studies predominantly address catastrophic forgetting within a simplified continual learning paradigm, typically confined to a singular data domain. Conversely, real-world applications frequently encompass multiple, evolving data domains, wherein models often struggle to retain many critical past information, thereby leading to performance degradation. This paper addresses this complex scenario by introducing a novel dynamic expansion approach called Learning Expandable and Adaptable Representations (LEAR). This framework orchestrates a collaborative backbone structure, comprising global and local backbones, designed to capture both general and task-specific representations. Leveraging this collaborative backbone, the proposed framework dynamically create a lightweight expert to delineate decision boundaries for each novel task, thereby facilitating the prediction process. To enhance new task learning, we introduce a novel Mutual Information-Based Prediction Alignment approach, which incrementally optimizes the global backbone via a mutual information metric, ensuring consistency in the prediction patterns of historical experts throughout the optimization phase. To mitigate network forgetting, we propose a Kullback–Leibler (KL) Divergence-Based Feature Alignment approach, which employs a probabilistic distance measure to prevent significant shifts in critical local representations. Furthermore, we introduce a novel Hilbert-Schmidt Independence Criterion (HSIC)-Based Collaborative Optimization approach, which encourages the local and global backbones to capture distinct semantic information in a collaborative manner, thereby mitigating information redundancy and enhancing model performance. Moreover, to accelerate new task learning, we propose a novel Expert Selection Mechanism that automatically identifies the most relevant expert based on data characteristics. This selected expert is then utilized to initialize a new expert, thereby fostering positive knowledge transfer. This approach also enables expert selection during the testing phase without requring any task information. Empirical results demonstrate that the proposed framework achieves state-of-the-art performance.

The advancement of artificial intelligence security research has led to the emergence of adversarial attack technology as a critical approach for identifying potential security vulnerabilities in artificial intelligence models . When targeting natural language processing models, conducting adversarial attacks in the hard-label setting presents a more practical and challenging black-box scenario due to the difficulty in computing gradients directly from discrete word sequences. Current textual adversarial attack methods are inefficient due to the lack of consideration for the limited number of queries available during the adversarial text generation process, creating a disparity between these approaches and real-world adversarial attack scenarios. To this end, this work proposes a dual-granularity optimization strategy that consists of a single-word semantic optimization and a multi-word joint semantic optimization procedure , and presents a query-efficient hard-label attack method called DualAttack by incorporating the proposed dual-granularity optimization strategy into the mutation and crossover process of the Genetic Algorithm framework. Extensive experimental results demonstrate that DualAttack can effectively produce high-quality adversarial texts with superior semantic similarity and minimal perturbation rates within fewer queries compared to existing methods in the hard-label setting.

Evaluating the potential risks posed by adversarial examples is crucial for securely deploying deep neural network (DNN)-based Internet of Things (IoT) devices. Although adversarial patches are considered a primary physical attack strategy, recent studies suggest their real-world impact may be less significant. Research has explored using optical tools like lasers or projectors to create perturbations, but these methods are uncommon in natural settings. Given the visual challenges inherent in natural environments, the prevalent occurrence of lens flare is noteworthy. This phenomenon can obstruct human vision and may also be exploited maliciously. In this article, we emphasize for the first time that lens flares produced by light sources exhibit strong adversarial characteristics. In the physical world, lens flare that occurs around and directly impacts the target object can easily deceive advanced models in various real-world scenarios. We show that merely using a regular flashlight to generate adversarial flare is sufficient to easily deceive well-trained models during both day and night in typical traffic situations, with an average attack success rate (ASR) of over 90%. Furthermore, by utilizing real lens flares as perturbations, we introduce a novel digital-domain closed-box adversarial attack method, primarily designed for extensive experimental validation of adversarial lens flare attacks in the physical world. Experimental results demonstrate that adversarial lens flare attacks effectively deceive state-of-the-art models, including YoloV8, DinoV2, and other three baseline models, achieving average ASRs as high as 95.4% on GTSRB and 82.8% on MTSD. We also discuss the limitations and defense mechanisms against this attack.

Background: Integral equations play a crucial role in modeling complex systems across various scientific disciplines. However, traditional numerical methods and existing physics-informed neural networks (PINNs) face substantial challenges, including the curse of dimensionality, uncontrolled error propagation, and limited generalization capabilities. Objectives: This paper aims to overcome these limitations by developing a robust and scalable solver for high-dimensional and nonlinear integral equations. The primary goal is to achieve higher accuracy and efficiency compared to traditional methods and existing deep learning approaches. Methods: We present the enhanced physics-informed neural network (EPINN), a novel framework that incorporates three key innovations: 1) a variable-order operator decomposition theory that transforms integral equations into well-posed differential systems, thereby mitigating error accumulation, 2) a differentiable primal function projection layer that ensures physical consistency within the Sobolev spaces, and 3) a boundary-aware multi-objective training paradigm that improves generalization. Results: Experimental validation across five benchmark cases spanning two to four dimensions, including linear/nonlinear Volterra/Fredholm and hybrid Volterra-Fredholm integral equations, demonstrates the superior performance of EPINN. Compared with traditional methods, EPINN reduces relative errors by 1 to 2 orders of magnitude, while achieving over 92% accuracy with limited training data. When compared with existing deep learning solvers, EPINN provides significant improvements in computational efficiency (with a speedup factor of 3 to 6 times) and accuracy (error reduction of 23% to 85%). Conclusions: These advancements establish EPINN as a robust and scalable solver for high-dimensional and nonlinear integral equations, with wide-ranging applications in computational physics and engineering. The success of EPINN suggests that integrating physical principles with neural networks can lead to substantial improvements in solving complex mathematical problems.

Identity-based encryption with equality test (IBEET) is a special form of searchable encryption that has broad applications in cloud computing. It enables users to perform equality tests on encrypted data without decryption, thereby achieving secure data search while ensuring data privacy and confidentiality. However, in the context of mobile cloud computing, the susceptibility of mobile devices to loss significantly increases the risk of private key exposure. Existing IBEET schemes struggle to address this issue effectively, limiting their practical applicability. Moreover, with the rapid advancement of quantum computing, the security of traditional cryptographic hardness assumptions faces potential threats. To address these challenges and enhance system efficiency, we proposes the first lattice-based revocable IBEET (RIBEET) scheme, which supports user key revocation. We prove that our scheme satisfies adaptive CCA security under the assumption of DLWE hard problem. Additionally, performance evaluations comparing our scheme with existing ones demonstrate that our scheme offers significant efficiency advantages. Furthermore, we apply the proposed scheme to mobile health services, showcasing its practicality and reliability in mobile cloud computing environments.

Currently, no practical lightweight authenticated key agreement (AKA) protocol with fine-grained pre-authentication has been developed to address security issues such as data integrity, authenticity, traceability, tamper-proofing, and privacy in 5G NR V2X. In this paper, we introduce a lightweight anonymous attribute-based signature of knowledge (Lw-AABSoK) scheme built on Curve25519 to defend against key-leakage attacks. This scheme enables attribute revocation and online credential updating, serving as a fine-grained pre-authentication cryptographic module for V2X secure communication. Leveraging the proposed Lw-AABSoK, we design an end-to-end fine-grained pre-authenticated key agreement protocol (E2E-FGpAKA). The E2E-FGpAKA is UDP-compatible; all interactive messages are self-validated by the Lw-AABSoK, and their sizes are strictly below the 5G NR MAC Transport Block Size (TBS). Through rigorous comparative analysis, scientific experimental verification, and comprehensive evaluation, it is evident that the proposed scheme holds significant practical value for 5G NR V2X.

Summary In this work, privacy‐preserving distributed deep learning (PPDDL) is re‐visited with a specific application to diagnosing long‐term illness like diabetic retinopathy. In order to protect the privacy of participants datasets, a multi‐key PPDDL solution is proposed which is robust against collusion attacks and is also post‐quantum robust. Additionally, the PPDDL solution provides robust network security in terms of integrity of transmitted ciphertexts and keys, forward secrecy, and prevention of man‐in‐the‐middle attacks and is extensively verified using Verifpal. Proposed solution is evaluated on retina image datasets to detect diabetic retinopathy, with deep learning accuracy results of 96.30%, 96.21% and 96.20% for DDL, DDL + SINGLE and DDL + MULTI scenarios respectively. Results from our simulation indicate that accuracy of the PPDDL is maintained while protecting the privacy of the datasets of participants. Our proposed solution is also efficient in terms of the communication and run‐time costs.

Authenticated Key agreement protocol (AKA) is one of the essential components for reliable secure communication in Industrial Internet-of-Things (IIoT) communication model. Recently, Srinivas et al. proposed a three-factor elliptic curve cryptosystem (ECC)-based AKA protocol called UAP-BCIoT for WSN-based intelligent transportation system (ITS). In this paper, we first find out that their protocol has a security weak point inherently called master secret disclose and key forgery defect which makes their protocol susceptible to variant impersonation attacks. To overcome the deficiency of their protocol, we construct an improved ECC-based three-factors (credential, password and biometric) tripartite authenticated key agreement protocol among managers Ui, domain gateway DG and IIoT nodes INj with identity dynamic revocation and online updating (IDR-OU-TAKA) for secure communication in IIoT. Unlike the vast majority of previous GWN-assisted MAKA protocols that only negotiate the session key between Ui and INj, our IDR-OU-TAKA protocol can selectively achieve Ui DG INj tripartite key negotiation according to Ui’s IPv6 addresses, meaning that any two parties can use the session key to establish a secure channel which can achieve isolation security within the IIoT domain. Besides, in our proposed IDR-OU-TAKA, the overdue or corrupted manager can be immediately revoked by dynamically maintaining the revocation list and the identity of manager can be securely updated online through an open channel. We give rigorous security proof based on real-or-random (ROR) model and the non-mathematical (informal) security analysis to our proposed IDR-OU-TAKA protocol. Finally, we conduct a comprehensive comparison and evaluation to our proposed IDR-OU-TAKA protocol with other state-of-art MAKA protocols in terms of security and functionality features, communication, and computation costs which clearly indicate that our protocol is more practical and suitable for IIoT.

Unmanned aerial vehicle (UAV)-enabled intelligent systems are emerging and empowering real-time monitoring and modeling tasks. The security requirements in real-time UAV-enabled intelligent systems are data integrity, authenticity, traceability, tamper-proofing, and privacy. A secure channel established by authenticated key agreement (AKA) protocol can cover all the security requirements. However, no UDP-based lightweight pairing-free AKA protocol has been proposed for the UAV system. In this article, we propose a UDP-compatible Curve25519-infrastructural identity-based end-to-end pre-AKA protocol (UDP-IBE2E-pAKA) with system auto-upgrading and direct and lifecycle credential revocation as a lightweight and reliable UDP-based secure communication module for UAV-enabled networks, which perfectly fits the rapid mobility and extremely harsh work environments of UAVs. To protect UAV-enabled systems stable from DDoS attacks, we construct an efficient identity-based signature as a preauthentication mechanism for the verifier to directly authenticate the sender without any redundant operations. In addition, to prevent the corrupted UAV from monitoring and disrupting attacks, our protocol can revoke the malicious entities directly and immediately with a revocation list in the authentication phase. Moreover, online mode auto-upgradable algorithms are designed to achieve key exposure resistance in our protocol. The full proof of the authenticity and privacy are given in this article. The comprehensive comparison with other state-of-the-art end-to-end AKA protocols indicates that our protocol meets the most robustness and highest efficiency on Raspberry Pi 5.

Access control and authenticity are two critical concerns of the encrypted propagating data in edge computing-assisted intelligent transportation systems (EC-ITS). This paper presents a fully traceable and verifiable ciphertext-policy attribute-based encryption scheme with auditable outsourced decryption and dynamic identity revocation (FTV-AOD-DR-CP-ABE) for EC-ITS as a confidential and fine-grained data sharing and acquiring module. The proposed FTV-AOD-DR-CP-ABE is computing-efficient that all the algorithms executed by vehicles including\(\mathbf{Enc}\),\(\mathbf{OutKeyGen}\)and\(\mathbf{FinalDec}\)are constant complexity. In addition, an efficient identity-based signature and message commitment (IBSMC) algorithm is constructed for the ciphertext and message in our FTV-AOD-DR-CP-ABE to provide both of them with traceable authenticity and verifiability. An outsourced key auditing algorithm\(\mathbf{TKAudit}\)is also innovated for RSU to audit the legality and freshness of outsourced key\(\mathsf{TK}\), which can protect the propagating data system against the flooding and DDoS attack with the illegal outsourced keys. Based on the traceability of the ciphertext and outsourced key, a dynamic vehicle revocation mechanism is designed in our scheme. Next the rigorous proofs of the data confidentiality, ciphertext and message traceable verifiability,\(\mathsf{TK}\)auditability and revocable security are given in random oracle model (ROM). Finally, by comprehensive comparison and evaluation of the proposed FTV-AOD-DR-CP-ABE with other state-of-the-art data propagating schemes, our FTV-AOD-DR-CP-ABE is more comprehensive.

Summary Due to successful applications of data analysis technologies in many fields, various institutions have accumulated a large amount of data to improve their services. As the speed of data collection has increased dramatically over the last few years, an increasing number of users are growing concerned about their personal information. Therefore, privacy preservation has become an urgent problem to be solved. Differential privacy as a strong privacy preservation tool has attracted significant attention. In this review, we focus on improving data utility of differentially private mechanisms through technologies related to cryptography. In particular, we first focus on how to improve data utility through anonymous communication. Then, we summarize how to improve data utility by combining differentially private mechanisms with homomorphic encryption schemes. Next, we summarize hardness results of what is impossible to achieve for differentially private mechanisms' data utility from the view of cryptography. Differential privacy borrowed intuitions from cryptography and still benefits from the progress of cryptography. To summarize the state‐of‐the‐art and to benefit future researches, we are motivated to provide this review.

Outsourced decryption attribute-based encryption (OD-ABE) is emerging as a promising cryptographic tool to provide efficient fine-grained access control for data accessing and sharing in cloud-assisted Intelligent Internet of Mobile Things (IIoMT). Decryption verification is an essential property of OD-ABE to enable the mobile user to verify the precision of the decryption data. Unfortunately, the most representative verification (commitment) algorithms have various security flaws. In this article, we first indicate that the two state-of-art key-based commitment schemes are vulnerable to “Commitment Extract(Decrypt)-then-Reuse Attack” and “Commitment Impersonation Attack” which demolish the unforgeability of the commitment. Then to cover all the existing attacks to commitment algorithms, we redefine a robuster verifiable security model for verifiable OD-ABE. Subsequently, we invent a ciphertext fingerprint (CTfp)-based commitment scheme and give rigorous proof to the proposed commitment scheme, including binding, hiding, unforgeability, and nonrepudiation (traceability) in the random oracle. Next, we apply our CTfp-based commitment to the widely used OD-ABE schemes to provide them robuster verifiability. Finally, the theoretical comparison and simulation experiments are presented to show our new type of commitment algorithm is more secure and practical.

Network slicing in a 6G environment is an important research area in the current years. However, satisfying the demands of network slice requests is a challenging task. Energy-efficient, secure, and Quality of Service (QoS) aware network slicing is important since network slices must share fewer amounts of resources. Further, implementing secure network slicing for software-defined networks (SDNs)/network function virtualization (NFV) is crucial. In this article, we tackle the issues, such as security, QoS, and resource consumption issues through network slicing and load balancing mechanisms in SDN/NFV assisted 6G environments. First, deep network slicing is implemented using generative adversarial network (GAN) for network slicing and management. Based on the slice capacity, slice priority, and QoS demands of network slices, GAN predicts the appropriate slice and links for data transmission. For each slice, the directed acyclic graph (DAG)-based blockchain technology is used in which traditional consensus is replaced by the Proof of Space (PoS) algorithm. A limitation of scalability and high resource consumption in the traditional blockchain is addressed in DAG-blockchain. To improve security, context-based authentication, and secure handover schemes are presented using the Markov decision making (MDM) and weighted product model, respectively. Then, higher load faced at the SDN controllers and switches are addressed by intruder packets classification and packets migration through hybrid neural decision tree (HyDNT) and Hybrid Political optimizer with a Heap-based Optimizer (HPoHO), respectively. To predict the load accurately, environment learning is implemented using the soft actor-critic (SAC) algorithm. Finally, the performance of the proposed SliceBlock model is evaluated.

Massive Internet of Things (IoT) data sets are possessed by big institutions serving daily life because IoT devices are widely used in our daily life such as wearable devices and smart home devices. Publishing these data sets among various institutions causes an increasing number of users to concern their personal privacy. Differential privacy is the state-of-the-art concept of privacy preservation, but it suffers from the low accuracy. In this article, we improve differentially private mechanisms including the Laplace mechanism as well as the sample and aggregation mechanism by bringing the personalized sampling technology into these mechanisms so that IoT data sets can be privately published through differentially private mechanisms. In particular, improved mechanisms assign a personalized sampling probability to each data record in a way that their accuracy can be improved. We analyse improved mechanisms in terms of their privacy and accuracy. Then, we empirically demonstrate that the performance of improved mechanisms is better than original mechanisms through extensive experiments on synthetic data sets and real-world data sets.

The need to capture intra-word information in natural language processing (NLP) tasks has inspired research in learning various word representations at word, character, or morpheme levels, but little attention has been given to syllables from a syllabic alphabet. Motivated by the success of compositional models in morphological languages, we present a Convolutional-long short term memory (Conv-LSTM) model for constructing Swahili word representation vectors from syllables. The unified architecture addresses the word agglutination and polysemous nature of Swahili by extracting high-level syllable features using a convolutional neural network (CNN) and then composes quality word embeddings with a long short term memory (LSTM). The word embeddings are then validated using a syllable-aware language model ( 31.267 ) and a part-of-speech (POS) tagging task ( 98.78 ), both yielding very competitive results to the state-of-art models in their respective domains. We further validate the language model using Xhosa and Shona, which are syllabic-based languages. The novelty of the study is in its capability to construct quality word embeddings from syllables using a hybrid model that does not use max-over-pool common in CNN and then the exploitation of these embeddings in POS tagging. Therefore, the study plays a crucial role in the processing of agglutinative and syllabic-based languages by contributing quality word embeddings from syllable embeddings, a robust Conv–LSTM model that learns syllables for not only language modeling and POS tagging, but also for other downstream NLP tasks.

Differential privacy is a widely accepted concept of privacy preservation, and the Laplace mechanism is a famous instance of differentially private mechanisms used to deal with numerical data. In this paper, we find that differential privacy does not take the linear property of queries into account, resulting in unexpected information leakage. Specifically, the linear property makes it possible to divide one query into two queries, such as$q(D)=q(D_{1})+q(D_{2})$if$D=D_{1}\cup D_{2}$and$D_{1}\cap D_{2}=\emptyset $. If attackers try to obtain an answer to$q(D)$, they can not only issue the query$q(D)$but also issue$q(D_{1})$and calculate$q(D_{2})$by themselves as long as they know$D_{2}$. Through different divisions of one query, attackers can obtain multiple different answers to the same query from differentially private mechanisms. However, from the attackers’ perspective and differentially private mechanisms’ perspective, the total consumed privacy budget is different if divisions are delicately designed. This difference leads to unexpected information leakage because the privacy budget is the key parameter for controlling the amount of information that is legally released from differentially private mechanisms. To demonstrate unexpected information leakage, we present a membership inference attack against the Laplace mechanism. Specifically, under the constraints of differential privacy, we propose a method for obtaining multiple independent identically distributed samples of answers to queries that satisfy the linear property. The proposed method is based on a linear property and some background knowledge of the attackers. When the background knowledge is sufficient, the proposed method can obtain a sufficient number of samples from differentially private mechanisms such that the total consumed privacy budget can be made unreasonably large. Based on the obtained samples, a hypothesis testing method is used to determine whether a target record is in a target dataset.

As data is growing in different dimensions, it is difficult to get appropriate data analytic tools. Spark is one of high speed "in-memory computing" big data analytic tool designed to improve the efficiency of data computing in both batch and realtime data analytic. Spark is memory bottleneck problem which degrades the performance of applications due to in memory computation and uses of storing intermediate and output result in memory. Investigating how performance is increased in relation to spark executor memory, number of executors, number of cores, and deploy mode parameters configuration in a standalone cluster model is our primary goal. Three representative spark applications are used as workloads to evaluates performance in relation to changing these parameters value. Experimental result show, submitting the job in cluster deploy mode is faster to finish than a submitting job in client deploy mode under two workloads. This implies spark performance does not depend on deploy mode rather it depends on types of application. However, increasing number of executor per worker, a number of core per executor and memory fraction will increase spark performance under all workloads in any deploy mode.

The differential privacy is the state-of-the-art conception for privacy preservation due to its strong privacy guarantees, however it suffers from low accuracy. In this paper, we propose a personalized sample Laplace mechanism by combining the Laplace mechanism with sampling technology. In order to improve the accuracy, the proposed mechanism assigns personalized sampling probability to each record. Based on the personalized sampling probability, we prove that the proposed mechanism satisfies ε differential privacy. Then we compare the proposed mechanism with other mechanisms in term of the accuracy. Through extensive experiments on synthetic data set and real world data set, we demonstrate that the performance of proposed mechanism is better.

The logistic model is a very elementary and important model in the field of machine learning. In this article, an efficient differential privacy logistic classification mechanism is proposed. The proposed mechanism is better than object function perturbation mechanism in terms of running time and accuracy. Regarding accuracy, the proposed mechanism's accuracy is almost the same as the no differential privacy (non-dp) mechanism, and the proposed mechanism is better than that of the object function perturbation mechanism in both the test accuracy and the train accuracy. As for the running time of the training model, the proposed mechanism is better than the object function mechanism and is the same as the non-dp mechanism.

Many RFID tags store valuable information that can easily be subject to unauthorized reading, leading to system security and privacy risks. The detection methods existed are not only complex and impractical, but also unable to extract more information about the abnormal signal. In this paper, we propose a lightweight detection approach for the unauthorized reading without affecting the operation of RFID systems. Such an approach contains three parts: RF signal scanner, signalevent model construction and abnormal feature extraction. In particular, we design and implement a RF scanner to acquire RF signals and measure RSSI values. After that, we build a signal-event model to analyze how the RSSI value is related to the RFID event. The detection of unauthorized reading is to investigate the deviation of observed RSSI values from their expected values. Finally, we extract and separate abnormal RSSI values to estimate the risk of unauthorized reading. The primary experimental results show that our approach can achieve high prediction accuracy in detecting unauthorized reading and make better performance in extracting abnormal features.

Recently, there has been growing interest in indoor localization, because numerous applications depend on the rapid and accurate position estimation of tagged objects. While RFID-based indoor localization is attractive, the need for a large-scale and high-density deployment of readers and reference tags is costly. Being the range-free localization, our schemes depend solely on mobile readers without reference tags or other devices, and it avoids the need of distance estimation according to RSSI or phase difference. We propose two novel algorithms, continuous scanning and category-based scheduling, for locating single and multiple tagged objects, respectively. Our primary experimental results show that the system can achieve high time efficiency and localization accuracy.

In many RFID applications, we conduct a RFID reader network to monitor a large number of tagged items. We also want to utilize this network to trace un-tagged items. In this paper, we propose ARNS-Adaptive RFID Network Scheduling scheme for device-free tracking. In particular, we firstly deploy a set of readers and reference tags into the monitored area. Then, we divide the tags within the read region of a reader into two groups, one in reader-to-reader collision area while the other not. We monitor the area by normal sampling model in which we need two phases to separately query the two groups. When an un-tagged target appears in the monitored area, we adopt tracking sampling model. In tracking sampling mode, the reader who detects the tags' frequency variations will adaptively query all tags in its read range to obtain the target's full trace. Meanwhile, we fill the missing path using shortest-path algorithm to recovery the trajectory. Experimental results show that ARNS achieves high tracking accuracy for certain applications.

In this paper, we consider the RFID reader coverage problem in three-dimensional space. We propose a cube-based model to describe both the reading region of readers and the area covered by reading regions. In our cube-based model, we place a reader into a large cube, which is equally cut into n small cubes. Then, we can use a mathematical three-dimensional matrix to model the reading region and covered area. Finally, we modify the particle swarm optimization (PSO) algorithm to maximize the coverage rate and minimize the overlap rate. Our simulation results show that the proposed model and algorithm are efficient for solving the RFID coverage problem in three-dimensional space.

Piece-related algorithms, including piece revelation, selection, and queuing, play a crucial role in the BitTorrent (BT) protocol, because the BT system can be viewed as a market where peers trade their pieces with one another. During the piece exchanging, a peer selects some pieces revealed by neighbors, and queues them up for downloading. In this paper, we provide a deep understanding of these algorithms, and also propose some improvements to them. Previous study has shown that the piece revelation strategy is vulnerable to under-reporting. We provide a game-theoretic analysis for this selfish gaming, and propose a distributed credit method to prevent it. Existing piece selection strategies, though long believed to be good enough, may fail to balance piece supply and demand. We propose a unified strategy to shorten the download time of peers by applying utility theory. The design of the piece queuing algorithm has a conflict with that of piece selection strategy, because it is not possible to assume that the queued requests for a selected piece can always be available on multiple neighbors. We give a possible fix to address the conflict by allowing peers to dynamically manage their unfulfilled requests. To evaluate the performance of the proposed algorithms, we run several experiments in a live swarm. Our primary results show that they can achieve fast individual and system-wide download time.

Bootstrapping is critical in any P2P network, since, on initial startup, a peer must bootstrap and find at least one neighbor. Existing P2P networks simply rely on centralized servers or static peers for bootstrapping, which may become a single point of failure. Recently, the shutdown of BT web-sites in China has caused a serious problem in BT bootstrapping. A decentralized way to bootstrap P2P networks is to clone existing social networks. In particular, a new peer obtains addresses of potential neighbors by sniffing instant messaging packets (e.g., MSN or QQ packets). With such addresses, the peer can bootstrap neighbors and join the network without the help of any centralized server.

Worm propagation analysis, including exploring mechanisms of worm propagation and formulating effects of network/worm parameters, has great importance for worm containment and host protection in P2P networks. Previous work only focuses on topological worm propagation where worms search a hosts neighbor-list to find new victims. In BitTorrent (BT) networks, the information from servers or trackers, however, could be fully exploited to design effective worms. In this paper, we propose a new approach for worm propagation in BT-like P2P networks. The worm, called Dynamic Hit-List (DHL) worm, locates new victims and propagates itself by requesting a tracker to build a dynamic hit list, which is a self-stopping BT worm to be stealthy. We construct an analytical model to study the propagation of such a worm: breadth-first propagation and depth-first propagation. The analytical results provide insights of the worm design into choosing parameters that enable the worm to stop itself after compromising a large fraction of vulnerable peers in a P2P network. We finally evaluate the performance of DHL worm through simulations. The simulation results verify the correctness of our model and show the effectiveness of the worm by comparing it with the topological worm.

A deniable authentication allows the receiver to identify the source of the received messages but cannot prove it to any third party. However, the deniability of the content, which is called restricted deniability in this paper, is concerned in electronic voting and some other similar application. At present, most non-interactive deniable authentication protocols cannot resist weaken key-compromise impersonation (W-KCI) attack. To settle this problem, a non-interactive identity-based restricted deniable authentication protocol is proposed. It not only can resist W-KCI attack but also has the properties of communication flexibility. It meets the security requirements such as correctness, restricted deniability as well. Therefore, this protocol can be applied in electronic voting.