Article
Computer Science, Hardware & Architecture
Yuchen Zhao, Isabel Wagner
Summary: This article studies 26 privacy metrics for graph anonymization and de-anonymization and evaluates their strength in terms of three criteria: monotonicity, evenness, and shared value range. The experiments show that no single metric fulfills all three criteria perfectly. Therefore, using multi-criteria decision analysis to aggregate multiple metrics into a metrics suite can improve the monotonicity of evaluations.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2022)
Article
Computer Science, Information Systems
Chandramohan Dhasarathan, Mohammad Kamrul Hasan, Shayla Islam, Salwani Abdullah, Umi Asma Mokhtar, Abdul Rehman Javed, Sam Goundar
Summary: This article discusses the analysis and prediction of COVID-19 data from patient data repository and highlights the risks to users' credentials and personal information. It proposes a homomorphic identification method to minimize the risk factors and emphasizes the importance of individual user privacy preservation.
COMPUTER COMMUNICATIONS
(2023)
Article
Computer Science, Information Systems
Bin Jiang, Jianqiang Li, Guanghui Yue, Houbing Song
Summary: The development of IoT has brought new changes and IIoT is promoting a new industrial revolution. With more IIoT applications, privacy protection issues are emerging. Differential privacy is used to protect user-terminal privacy, requiring in-depth research.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Theory & Methods
Wenjing Zhang, Bo Jiang, Ming Li, Xiaodong Lin
Summary: This paper proposes an optimal centralized privacy-preserving aggregate mobility data release mechanism that minimizes information leakage by releasing perturbed versions of the raw aggregate location. The user-level and aggregate-level privacy leakage is measured using mutual information, and leakage minimization problems are formulated under utility constraints. Reinforcement learning models and an efficient RL algorithm are employed to derive the optimal solutions.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2022)
Article
Computer Science, Information Systems
Mohamed Ashraf, Sherine Rady, Tamer Abdelkader, Tarek F. Gharib
Summary: Privacy Preserving Utility Mining (PPUM) is proposed to balance utility maximization and privacy preservation. However, there is a lack of scalable and efficient privacy preserving algorithms for handling large and dense datasets. This paper proposes three heuristic-based algorithms to efficiently conceal all sensitive high utility itemsets while mitigating the impact on non-sensitive information.
COMPUTERS & SECURITY
(2023)
Article
Computer Science, Theory & Methods
Qiongxiu Li, Jaron Skovsted Gundersen, Richard Heusdens, Mads Graesboll Christensen
Summary: Privacy-preserving distributed processing is a hot topic, with different existing algorithms and metrics making it challenging to choose the most appropriate algorithm. This paper proposes information-theoretical metrics based on mutual information, to compare and relate algorithms in terms of output utility and individual privacy. By considering adversary models and deriving a lower bound on individual privacy, insights are provided for selecting the preferred algorithm under different conditions.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2021)
Article
Computer Science, Information Systems
Jingyu Jia, Chang Tan, Zhewei Liu, Xinhao Li, Zheli Liu, Siyi Lv, Changyu Dong
Summary: Differential privacy (DP) is a general approach for defending against inference attacks, but striking a balance between privacy and utility is difficult for complex data analysis tasks. Total variation distance (TVD) privacy, a weaker privacy definition with more accurate privacy risk estimation, is a potential solution. In this paper, we systematically study TVD privacy, analyze its theoretical aspects, and demonstrate its effectiveness in measuring privacy risks through membership inference attacks. Our work suggests that TVD privacy is a valuable tool for estimating privacy risks and has the potential to become a widely-used privacy definition.
INFORMATION SCIENCES
(2023)
Article
Biodiversity Conservation
Irene Petrosillo, Maria Victoria Marinelli, Giovanni Zurlini, Donatella Valente
Summary: This research examines the impact of landscape heterogeneity and Xylella fastidiosa infection on pollination services through multi-scale spatial assessment and multi-temporal analysis. The results demonstrate significant changes in landscape functioning in provinces affected by the infection, highlighting the loss of permanent land-covers such as olive groves.
ECOLOGICAL INDICATORS
(2022)
Article
Multidisciplinary Sciences
Muhammad Azeem, Javid Shabbir, Najma Salahuddin, Sundus Hussain, Muhammad Azeem, Javid Shabbir, Najma Salahuddin, Sundus Hussain, Musarrat Ijaz
Summary: The randomized response technique is commonly used for collecting reliable information in social surveys. However, previous studies tend to hide situations where other models perform better, resulting in biased comparisons. Our neutral comparative study of four quantitative randomized response techniques reveals that different models have varying effectiveness depending on the situation, and we have also determined the mathematical conditions for their efficiency.
Article
Construction & Building Technology
Khanh Quoc Tran, Vinh Quang Trinh, Duong Thai Nguyen, Stefan Klir, Babak Zandi, Alexander Herzog
Summary: In this study, the mixed light was measured using a spectrometer and an RGB sensor, and the correlation between them was verified. The results show that a commercially available RGB sensor can accurately measure the visual and non-visual parameters of lighting systems after proper processing.
BUILDING AND ENVIRONMENT
(2023)
Article
Multidisciplinary Sciences
Muhammad Umair, Mashkoor Mohsin, Ute Wolff Soenksen, Timothy Rutland Walsh, Lothar Kreienbrock, Ramanan Laxminarayan
Summary: Global health and economies are expected to be severely affected by antimicrobial resistance (AMR), and three organizations are working to prevent future crises. Collaborative efforts are needed to develop and implement a globally accepted antimicrobial use metric system for reliable comparisons among different data sets.
Article
Engineering, Multidisciplinary
Chandula T. Wickramarachchi, Eoghan Maguire, Elizabeth J. Cross, Keith Worden
Summary: Population-based structural health monitoring expands the scope of structural health monitoring from a single structure to a group of structures, allowing inferences and knowledge transfer within and between populations. This paper focuses on assessing the similarity of structures at the beginning of the analysis chain using distance metrics, to quickly identify abnormalities, group similar structures, and inform further decisions.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Clinical Neurology
Dorothee Fischer, Elizabeth B. Klerman, Andrew J. K. Phillips
Summary: Sleep regularity is important for health outcomes, but there is currently no systematic approach to measuring it; new metrics like CPD and SRI show promise in capturing variability between consecutive days; different metrics perform differently in various scenarios, and selecting the most appropriate metric depends on factors such as data type, study length, and sample size.
Article
Biochemical Research Methods
Mingtan Dong, Zhenbing She, Xiong Xiong, Guang Ouyang, Zejiao Luo
Summary: The traditional manual analysis of microplastics has its drawbacks, such as being labor-intensive, inaccurate in identification, and lacking uniformity. Automated analysis strategies based on vibrational spectroscopy, including laser direct infrared, Raman, and focal plane array-Fourier transform infrared imaging, have been developed. Each strategy has its advantages and limitations in terms of quantification, detection limit, size measurement, and material identification accuracy and speed.
ANALYTICAL AND BIOANALYTICAL CHEMISTRY
(2022)
Article
Computer Science, Theory & Methods
Hatef Otroshi Shahreza, Yanina Y. Shkel, Sebastien Marcel
Summary: As the applications of biometric recognition systems are increasing rapidly, there is a growing need to secure the sensitive data used within these systems. In this paper, we propose a new method for measuring the linkability of protected biometric templates based on maximal leakage, which is a well-studied measure in information-theoretic literature.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
