Article
Computer Science, Information Systems
Brijesh B. Mehta, Udai Pratap Rao
Summary: This paper discusses the challenges of privacy preservation in big data analytics and proposes an improved method ImSLD based on scalable k-anonymization. By testing on poker dataset within the MapReduce framework, significant improvements in running time and lower information loss were demonstrated compared to existing methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Xuyun Zhang, Lianyong Qi, Wanchun Dou, Qiang He, Christopher Leckie, Ramamohanarao Kotagiri, Zoran Salcic
Summary: Scalable data processing platforms built on cloud computing are becoming more attractive for supporting big data applications. However, privacy concerns hinder the use of public cloud platforms. In this paper, a scalable approach based on MapReduce is proposed to address the scalability issues of multidimensional anonymisation when handling big data. The approach also demonstrates applicability to differential privacy.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Article
Computer Science, Information Systems
Keke Gai, Meikang Qiu, Hui Zhao
Summary: With the rapid growth of big data applications in cloud computing, privacy has become a significant concern. While the implementation of emerging technologies has brought many benefits and improved application performance, challenges such as data encryption execution time have also emerged. Many current applications prioritize performance over privacy by abandoning data encryption.
IEEE TRANSACTIONS ON BIG DATA
(2021)
Article
Computer Science, Information Systems
Lo'ai A. Tawalbeh, Gokay Saldamli
Summary: Large scale distributed systems, especially cloud and mobile cloud deployments, offer services that improve quality of life and organizational efficiency. Data-driven applications are popular and successful, but also bring challenges like storage, big data processing, and privacy concerns. Solutions using cloud computing, P2P systems, and hybrid mobile cloud models can enhance performance and address these issues.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Chunchun Ni, Li Shan Cang, Prosanta Gope, Geyong Min
Summary: The growth of big data can increase risks of re-identification in complex IoT environment. This work proposes a data anonymisation evaluation framework that can evaluate commonly used data anonymization algorithms in terms of privacy preserving level, data utility, and performance. Experiment results demonstrate that the proposed solution can effectively evaluate the performance and de-identification features that can help to prevent inappropriate usage of anonymized data.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Ashutosh Kumar Singh, Niharika Singh, Ishu Gupta
Summary: This paper proposes a privacy-preserving hierarchy-oriented collaborative architecture for data storage in the cloud and designs an algorithm to enhance privacy. By isolating sensitive and non-sensitive records, the data is protected from being compromised by attackers, and the efficiency of data restructuring is improved by incorporating the unification level. The findings also include a statistical study on the behavior of the properties used and complexity analysis of the work.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Abir El Azzaoui, Pradip Kumar Sharma, Jong Hyuk Park
Summary: This paper presents an efficient, scalable, and secure solution for complex Smart Healthcare computations using a Quantum Cloud-as-a-service. The usage of Quantum Terminal Machines (QTM) and Blockchain technology enhances the feasibility and security of the proposed architecture.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2022)
Article
Automation & Control Systems
Bin Zhao, Kai Fan, Kan Yang, Zilong Wang, Hui Li, Yintang Yang
Summary: This article presents a privacy-preserving federated learning scheme for mining industrial big data, exploring the impact of shared parameter proportions on accuracy through experiments. It is found that sharing partial parameters can almost achieve the same accuracy as sharing all parameters, reducing privacy leakage.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Kun Wang, Jiahui Yu, Xiulong Liu, Song Guo
Summary: With the increasing demand for big data storage due to the growing amounts of data, privacy protection becomes a crucial issue in data sharing. The proposed pre-authentication mechanism enhances system security by combining various techniques.
IEEE TRANSACTIONS ON BIG DATA
(2021)
Article
Computer Science, Information Systems
Xingfu Yan, Wing W. Y. Ng, Biao Zeng, Bowen Zhao, Fucai Luo, Ying Gao
Summary: Providing appropriate rewards in mobile crowdsensing is an effective way to motivate participants, but the privacy of participants is not well protected in most incentive schemes. To address these issues, a privacy-preserving and source-reliable incentive mechanism scheme called P2SIM is proposed, which combines redactable signature with private hash function to verify the source reliability of sensing data without revealing participants' privacy. The rewards are divided into fixed rewards and floating rewards to enhance flexibility in distribution.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Paulo Silva, Edmundo Monteiro, Paulo Simoes
Summary: This article discusses the issues of security and privacy assurances in the transmission and storage of private data online, analyzing various privacy threats, concepts, regulations, and personal data types, as well as Privacy Enhancing Technologies and anonymization mechanisms. Additionally, it also discusses privacy tools, models, and metrics, along with the current research challenges in achieving higher privacy levels in cloud services.
Article
Computer Science, Information Systems
Mohammed BinJubier, Mohd Arfian Ismail, Abdulghani Ali Ahmed, Ali Safaa Sadiq
Summary: Publishing big data is important for knowledge building, but it raises privacy concerns. Existing methods have drawbacks and risks. This study proposes a new method to protect privacy and maintain data utility.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Automation & Control Systems
Hongliang Bi, Jiajia Liu, Nei Kato
Summary: With the development of the industrial Internet of Things (IIoT), intelligent healthcare aims to monitor users' health information remotely. However, the security of privacy information in the data collected from wearable devices has been overlooked. This article proposes a deep learning-based privacy preservation and data analytics system to solve this problem.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Lu Yang, Xingshu Chen, Yonggang Luo, Xiao Lan, Wei Wang
Summary: The prevalence of missing values in real environments' data streams cannot be ignored in data stream privacy preservation. However, most privacy preservation methods currently developed do not consider missing values. This study proposes a utility-enhanced approach called Incomplete Data strEam Anonymization (IDEA) to balance the utility and privacy preservation of incomplete data streams. The proposed approach introduces a slide-window-based processing framework to continuously anonymize data streams and enables clustering between incomplete records and complete records to generate clusters with minimal information loss. Furthermore, a generalization method based on maybe match is proposed to avoid missing value pollution.
TSINGHUA SCIENCE AND TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Xiaotong Wu, Taotao Wu, Maqbool Khan, Qiang Ni, Wanchun Dou
Summary: Privacy preservation in big data is a critical issue, with differential privacy mechanism vulnerable when multiple datasets are correlated. This leads to a shift from a trade-off between privacy and utility to a game problem. The paper introduces the concept of correlated differential privacy and analyzes the existence and uniqueness of pure Nash Equilibrium in a game model of multiple players publishing data sanitized by differential privacy.
IEEE TRANSACTIONS ON BIG DATA
(2021)
Article
Computer Science, Software Engineering
Hongsheng Hu, Gillian Dobbie, Zoran Salcic, Meng Liu, Jianbing Zhang, Lingjuan Lyu, Xuyun Zhang
Summary: Recommender systems are important in big data analytics for their potential to bring high profit. However, privacy concerns and regulations make it difficult to integrate scattered data. Existing privacy-preserving recommender system models based on cryptography lack flexibility. This paper proposes a differentially private LSH approach that guarantees privacy preservation while offering efficient and accurate recommendations.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Viet Vo, Xingliang Yuan, Shi-Feng Sun, Joseph K. K. Liu, Surya Nepal, Cong Wang
Summary: The increasing adoption of cloud storage systems is driven by the need for cost-effective and easy-to-access solutions as businesses and governments move away from in-house data servers. However, the lack of security in cloud storage has led to numerous large-scale data breaches. To address this issue, this paper introduces ShieldDB, an encrypted document database that incorporates searchable encryption technique while maintaining scalability. A tailored padding countermeasure is implemented to protect against real-world threats and ensure continuous obfuscation of the access pattern to the database. The authors present a comprehensive implementation of ShieldDB and conduct extensive evaluations on Azure Cloud.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Kai Zhang, Jiao Tian, Hongwang Xiao, Ying Zhao, Wenyu Zhao, Jinjun Chen
Summary: Blockchain has attracted attention from the IoT research community due to its decentralization and consistency. However, the accessibility of all nodes to the chain data raises privacy concerns. To address this issue, we propose a novel LDP mechanism that splits and perturbs input numerical data using digital bits, without requiring a fixed input range and large data volume. Our adaptive privacy budget allocation model significantly reduces the deviation of the perturbation function and provides high data utility while maintaining privacy.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Rongjunchen Zhang, Tingmin Wu, Sheng Wen, Surya Nepal, Cecile Paris, Yang Xiang
Summary: This article presents a novel chatbot model called SAM, which is based on Semantic Awareness Matching. SAM utilizes a two-layer matching network to capture similarity and semantic features in the context, and selects appropriate responses based on the matching probability of the two feature types.
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Xiyuan Hu, Jingpeng Sun, Jinping Dong, Xuyun Zhang
Summary: We proposed a deep learning-based accurate snore detection model for long-term home monitoring of snoring during sleep. The model outperformed other traditional approaches and deep learning models in terms of snore detection. It predicted candidate boxes and confidence scores based on the feature maps derived by the feature extraction network.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Lianyong Qi, Xiaolong Xu, Xiaotong Wu, Qiang Ni, Yuan Yuan, Xuyun Zhang
Summary: This paper investigates the opportunities and challenges of mobile video streaming services in the sixth generation (6G) network using digital twin technology and cloud-centric architecture. The authors propose a solution using crowdsourcing to attract mobile users to follow a specified path and share network resources, alleviating the problem of increasing traffic demands. They present algorithms for user recruitment optimization and evaluate the system performance through extensive experiments.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Yanjun Zhang, Guangdong Bai, Xue Li, Surya Nepal, Marthie Grobler, Chen Chen, Ryan K. L. Ko
Summary: Genome-wide analysis has health and social benefits, but sharing such data may risk revealing sensitive information. Identity tracing attack exploits correlations among genomic data to reveal the identity of DNA samples. This paper proposes a framework called "F-RAG" to enable privacy-preserving data sharing and computation in genome-wide analysis, mitigating privacy risks without compromising utility.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2023)
Article
Computer Science, Hardware & Architecture
Yifeng Zheng, Cong Wang, Ruochen Wang, Huayi Duan, Surya Nepal
Summary: In this paper, a new system is designed, implemented and evaluated to enable efficient outsourcing of decision tree inference to the cloud, improving the online end-to-end secure inference latency at the cloud and the local-side performance of the model provider. The paper presents a scheme that securely shifts most of the processing of the model provider to the cloud, reducing the model provider's performance complexities. Additionally, a scheme is devised to optimize the performance of secure decision tree inference at the cloud, specifically the communication round complexities. The new system achieves up to 8x better online end-to-end secure inference latency at the cloud side and brings the model provider up to 19x savings in communication and 18x savings in computation.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2023)
Editorial Material
Environmental Sciences
Carlos Enrique Montenegro Marin, Xuyun Zhang, Nallappan Gunasekaran
JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Shuo Wang, Shangyu Chen, Tianle Chen, Surya Nepal, Carsten Rudolph, Marthie Grobler
Summary: This study proposes an adversarial attack strategy that implements fine-granularity, semantic-meaning-oriented structural perturbations on images. The method manipulates the semantic attributes of images through the use of disentangled latent codes to construct adversarial perturbations. The empirical evaluations demonstrate the strong attack capability of this method against black-box classifiers and establish the existence of a universal semantic adversarial example.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Cybernetics
Xingjuan Cai, Wanwan Guo, Mengkai Zhao, Zhihua Cui, Jinjun Chen
Summary: This article proposes a knowledge graph-based many-objective model for explainable social recommendation (KGMESR), which considers the explainability, accuracy, novelty, and content quality of social recommendation results. The model utilizes social behavior information to calculate user similarity and quantifies the explainability of results using entity vectors and embedding vectors. A many-objective recommendation algorithm based on the partition deletion strategy is designed for efficiency. Experimental results demonstrate preferable recommendation results and two case studies affirm the explainability of the proposed model.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Yi Chen, Zoran Salcic, Hongxia Wang, Kim-Kwang Raymond Choo, Xuyun Zhang
Summary: This article proposes a joint distortion-based non-additive cost assignment method to reduce distortion drift and improve security in video steganography. Extensive experiments show that the proposed method achieves enhanced security and visual stego video quality.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Lianyong Qi, Wenmin Lin, Xuyun Zhang, Wanchun Dou, Xiaolong Xu, Jinjun Chen
Summary: Using Web APIs registered in service sharing communities for mobile APP development can reduce development period and cost. However, the large number and differences of available APIs make it difficult for selection. To address this challenge, a correlation graph-based approach is proposed for personalized and compatible Web APIs recommendation in mobile APP development. Extensive experiments on a real dataset prove the feasibility of the proposed recommendation approach.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Hardware & Architecture
Fei Zhu, Xun Yi, Alsharif Abuadbba, Ibrahim Khalil, Surya Nepal, Xinyi Huang
Summary: The healthcare IoT is a valuable tool in the healthcare industry. However, sharing data raises security and privacy concerns. Redactable signature schemes (RSSs) offer a solution by allowing the deletion of privacy-sensitive data without invalidating the signature. However, existing RSSs have issues with key management and computation overheads. To address these challenges, this work proposes certificateless RSS and provides an efficient scheme with enhanced security and batch verification.
IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING
(2023)
Article
Computer Science, Information Systems
Shabnam Kasra Kermanshahi, Ron Steinfeld, Xun Yi, Joseph K. Liu, Surya Nepal, Junwei Lou
Summary: This paper introduces MDRSSE, a novel symmetric searchable encryption scheme specifically tailored for multi-dimensional range search. It efficiently supports multi-dimensional range search without incurring undetermined additional communication or computation costs, and achieves semantic security.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Flavia Bonomo-Braberman, Nick Brettell, Andrea Munaro, Daniel Paulusma
Summary: This article discusses the convexity and mim-width of bipartite graphs, and it proves that for certain families of graphs 7-t, the 7-t-convex graphs can be solved in polynomial time for NP-complete problems. It also explores the bounded and unbounded mim-width of 7-t-convex graphs for different sets 7-t.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Keqin Li
Summary: In this paper, we propose a computation offloading strategy to satisfy all UEs served by an MEC and develop an efficient method to find such a strategy. By using Markov chains to characterize UE mobility and calculating the joint probability distribution of UE locations, we can obtain the average response time of UEs and predict the overall average response time of tasks. Additionally, we solve the power constrained MEC speed setting problem.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Correction
Computer Science, Hardware & Architecture
Peter L. Bartlett, Philip M. Long
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Philipp Czerner, Roland Guttenberg, Martin Helfrich, Javier Esparza
Summary: This paper presents a construction method that produces population protocols with a small number of states, while achieving near-optimal expected number of interactions, for deciding Presburger predicates.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Katharina T. Huber, Leo van Iersel, Remie Janssen, Mark Jones, Vincent Moulton, Yukihiro Murakami, Charles Semple
Summary: This paper investigates the relationship between undirected and directed phylogenetic networks, and provides corresponding algorithms. The study reveals that the directed phylogenetic network is unique under specific conditions. Additionally, an algorithm for directing undirected binary networks is described, applicable to certain classes of directed phylogenetic networks.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Francesco Betti Sorbelli, Alfredo Navarra, Lorenzo Palazzetti, Cristina M. Pinotti, Giuseppe Prencipe
Summary: This study discusses the deployment of IoT sensors in an area that needs to be monitored. Drones are used to collect data from the sensors, but they have energy and storage constraints. To maximize the overall reward from the collected data and ensure compliance with energy and storage limits, an optimization problem called Multiple-drone Data-collection Maximization Problem (MDMP) is proposed and solved using an Integer Linear Programming algorithm.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Carla Binucci, Emilio Di Giacomo, William J. Lenhart, Giuseppe Liotta, Fabrizio Montecchiani, Martin Nollenburg, Antonios Symvonis
Summary: In this study, we investigate the problem of representing a graph as a storyplan, which is a model for dynamic graph visualization. We prove the NP-completeness of this problem and propose two parameterized algorithms as solutions. We also demonstrate that partial 3-trees always admit a storyplan and can be computed in linear time. Additionally, we show that even if the vertex appearance order is given, the problem of choosing how to draw the frames remains NP-complete.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
Article
Computer Science, Hardware & Architecture
Leszek Gasieniec, Tomasz Jurdzinski, Ralf Klasing, Christos Levcopoulos, Andrzej Lingas, Jie Min, Tomasz Radzik
Summary: This passage describes the Bamboo Garden Trimming Problem and presents approximation algorithms for both Discrete BGT and Continuous BGT.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2024)
