Article
Computer Science, Information Systems
Edgar Batista, Antoni Martinez-Balleste, Agusti Solanas
Summary: The proper utilization of process mining techniques with large amounts of event data can lead to the discovery, monitoring, and improvement of business processes, enabling the development of more efficient business intelligence systems. However, privacy concerns arising from personal and confidential information within event data have not been adequately addressed in the field of process mining. This article presents a novel privacy-preserving process mining method called k-PPPM, which utilizes microaggregation techniques to achieve k-anonymity and protects targeted individuals from re-identification through attacks based on process model analysis and location-oriented attacks.
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Josep Domingo-Ferrer, Sergio Martinez, David Sanchez
Summary: This paper discusses the importance of mobility data and proposes a decentralized approach to anonymize trajectories while protecting privacy. By aggregating with similar trajectories, a k-anonymized mobility dataset is constructed.
COMPUTER COMMUNICATIONS
(2022)
Article
Public, Environmental & Occupational Health
J. Andrew, R. Jennifer Eunice, J. Karthikeyan
Summary: Digital health data collection is important but challenging due to privacy concerns. Existing research studies have limitations such as involving third-party anonymizers or private channels. This article proposes a novel approach that anonymizes healthcare data without third-party involvement and restricts communication to elected representatives. The proposed protocol overcomes privacy attacks and outperforms state-of-the-art techniques in privacy protection and computational complexity.
FRONTIERS IN PUBLIC HEALTH
(2023)
Article
Computer Science, Information Systems
Jianxi Yang, Manoranjan Dash, Sin G. Teo
Summary: With the rapid advancement of mobile phone technology, location-based services rely heavily on user mobility data which poses privacy concerns. Effective privacy preservation algorithms for trajectory data are essential to balance utility and privacy for mobile users. The proposed Privacy-Preserving Trajectory Publication Framework for CDR offers a novel approach for anonymizing trajectory data, catering to user privacy and service efficiency.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2021)
Article
Computer Science, Information Systems
Kah Meng Chong, Amizah Malip
Summary: Publishing patient data without revealing sensitive information is a challenging research issue in the healthcare sector. This paper introduces two new privacy notions, namely identity unlinkability and attribute unlinkability, and designs schemes to address identity and attribute disclosure problems while preserving data utility. Experimental results demonstrate the effectiveness of our schemes in achieving both data utility preservation and privacy protection simultaneously.
COMPUTER COMMUNICATIONS
(2022)
Article
Computer Science, Information Systems
Lynda Kacha, Abdelhafid Zitouni, Mahieddine Djoudi
Summary: K-anonymity is a widely used approach for privacy preservation in microdata, but it suffers from information loss. To address this issue, this paper proposes a novel algorithm based on the Black Hole Algorithm, which improves data utility.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Zhixiang Zhang, Thomas Cilloni, Charles Walter, Charles Fleming
Summary: The paper introduces a novel privacy-preserving video algorithm that utilizes semantic segmentation and adaptive blurring to identify and anonymize objects of different scales, while maintaining the meaning in the visual data.
Article
Multidisciplinary Sciences
Yan Yan, Anselme Herman Eyeleko, Adnan Mahmood, Jing Li, Zhuoyue Dong, Fei Xu
Summary: The rapid development of the mobile Internet and widespread use of intelligent terminals have accelerated the digitization of personal information and the evolution of the big data era. Sharing and publishing big data bring convenience but also increase the risk of personal privacy leakage. To reduce privacy leakage caused by data release, various privacy preserving data publishing methods have been proposed. However, non-numerical sensitive information may still have semantic relevance, leading to serious privacy disclosures. This paper introduces a privacy preserving dynamic data publishing method based on microaggregation to address this issue, which shows better privacy protection and availability of published data compared to existing methods.
SCIENTIFIC REPORTS
(2022)
Article
Engineering, Multidisciplinary
Muhammad Arif, Jianer Chen, Guojun Wang, Oana Geman, Valentina Emilia Balas
Summary: In Vehicular Ad-hoc Networks, Location-based Services provide personalized services to clients based on their movement characteristics, but privacy protection is a challenge. Proposed Differential Privacy and generalization based anonymization approach aims to protect sensitive vehicular trajectories. Experiments show good data feasibility and efficiency of the method, as well as the impact of privacy budget values on error rates.
Article
Computer Science, Information Systems
Philip Derbeko, Shlomi Dolev, Ehud Gudes, Jeffrey D. Ullman
Summary: In recent years, the increasing amount of data collected from different and often non-cooperative databases has posed challenges for privacy-preserving distributed calculations. This paper proposes a sampling method to improve computational performance and discusses an analysis of error bounds. Experimental results confirm the validity of the approach.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Article
Computer Science, Information Systems
J. Andrew Onesimu, J. Karthikeyan, Jennifer Eunice, Marc Pomplun, Hien Dang
Summary: Advancements in Industry 4.0 have brought significant improvements to the healthcare sector. However, sharing healthcare data while preserving privacy is challenging due to security concerns. This paper presents an attribute-focused privacy preserving data publishing scheme that combines fixed-interval and improved l-diverse slicing approaches. Experimental results show improved accuracy and reduced information loss compared to existing methods. The proposed scheme provides data utility while protecting against various privacy breaches.
Article
Computer Science, Information Systems
Stephan A. Fahrenkrog-Petersen, Martin Kabierski, Han van der Aa, Matthias Weidlich
Summary: Information systems support business process execution and data about process execution is recorded in event logs for analysis. To protect personal information, anonymization techniques should be used. This paper presents two approaches, SaCoFa and SaPa, for anonymizing the control-flow of a process.
INFORMATION SYSTEMS
(2023)
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
Abdul Majeed, Sungchang Lee
Summary: Anonymization is a practical solution for protecting user privacy, with many data owners anonymizing data to safeguard user privacy. This paper systematically investigates relational and structural anonymization techniques, categorizes and evaluates existing anonymization methods, and discusses the challenges and research directions in privacy preserving data publishing involving social network and relational data.
Article
Computer Science, Information Systems
J. Andrew Onesimu, J. Karthikeyan, Yuichi Sei
Summary: The healthcare services industry has undergone significant changes with the rise of IoT, leading to concerns about privacy of patient data. By utilizing a clustering-based anonymity model, an efficient privacy-preserving scheme has been proposed to address privacy concerns and prevent various attacks in healthcare IoT systems.
PEER-TO-PEER NETWORKING AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Polina Rozenshtein, Nikolaj Tatti, Aristides Gionis
Summary: This paper investigates the problem of determining entity activity based on interactions, proposing two formulations and efficient algorithms for untangling networks. While the sum problem is shown to be NP-hard, the max problem can be solved optimally in linear time. In cases of multiple activity intervals per entity, both formulations are proved to be inapproximable but efficient algorithms based on alternative optimization are proposed. Evaluation on synthetic and real-world datasets supports the validity of concepts and performance of algorithms.
DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
Article
Computer Science, Artificial Intelligence
Antonis Matakos, Aristides Gionis
Summary: Online social networks offer numerous benefits such as establishing new connections, gaining knowledge about the world, exposure to diverse viewpoints, and access to previously inaccessible information. This research focuses on leveraging the triadic closure principle to develop methods that foster new connections and improve the flow of information in the network.
DATA MINING AND KNOWLEDGE DISCOVERY
(2022)
Article
Computer Science, Artificial Intelligence
Bruno Ordozgoiti, Ananth Mahadevan, Antonis Matakos, Aristides Gionis
Summary: When searching for information in a data collection, it is often important to not only find relevant items but also assemble a diverse set to explore different concepts in the data. This paper addresses the problem of finding a diverse set of items when item relatedness is measured by a similarity function. The authors propose a new minimization objective and employ a randomized rounding strategy to find good solutions efficiently. They also introduce a novel bound for the ratio of Poisson-Binomial densities, which has applications beyond this problem. The proposed algorithm outperforms greedy approaches commonly used in the literature according to experiments on benchmark datasets.
DATA MINING AND KNOWLEDGE DISCOVERY
(2022)
Article
Computer Science, Artificial Intelligence
Guangyi Zhang, Nikolaj Tatti, Aristides Gionis
Summary: Submodular maximization is fundamental in many important machine learning problems and has various applications. However, the study of maximizing submodular functions has often been limited to selecting a set of items, while many real-world applications require a ranking solution. This paper introduces a novel formulation for ranking items with submodular valuations and budget constraints, and proposes practical algorithms with approximation guarantees for different types of budget constraints. The empirical evaluation shows that the proposed algorithms outperform strong baselines.
DATA MINING AND KNOWLEDGE DISCOVERY
(2022)
Article
Computer Science, Artificial Intelligence
Antonis Matakos, Cigdem Aslay, Esther Galbrun, Aristides Gionis
Summary: Social-media platforms have provided new ways for citizens to participate in public debates and stay informed. This paper proposes a novel approach to maximize the diversity of exposure in a social network, ensuring citizens are exposed to diverse viewpoints for a healthy information sharing environment.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Proceedings Paper
Computer Science, Information Systems
Martino Ciaperoni, Aristides Gionis, Athanasios Katsamanis, Panagiotis Karras
Summary: This paper presents an algorithm called SIEVE, which is an improvement on the Viterbi algorithm to address the issue of its space complexity growing with the number of observations. SIEVE improves space efficiency by discarding and recomputing parts of the DP solution, without incurring a time complexity overhead.
PROCEEDINGS OF THE 2022 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA (SIGMOD '22)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Suhas Thejaswi, Bruno Ordozgoiti, Aristides Gionis
Summary: The study introduces a novel problem of diversity-aware clustering, where potential cluster centers belong to groups defined by protected attributes. It shows that the diversity-aware k-median problem is NP-hard in general cases but approximation algorithms can be obtained when facility groups are disjoint. Experimentally, approximation methods are evaluated for tractable cases, and a relaxation-based heuristic is provided for theoretically intractable scenarios.
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2021: RESEARCH TRACK, PT II
(2021)
Proceedings Paper
Computer Science, Information Systems
Cigdem Aslay, Martino Ciaperoni, Aristides Gionis, Michael Mathioudakis
Summary: Bayesian networks are probabilistic models capturing dependencies among variables, with Variable Elimination being a fundamental algorithm for probabilistic inference. This paper proposes a novel materialization method to enhance efficiency in processing inference queries. Experimental results show that moderate materialization can significantly improve query running time.
2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2021)
(2021)
Article
Computer Science, Artificial Intelligence
Gennady Andrienko, Natalia Andrienko, Chiara Boldrini, Guido Caldarelli, Paolo Cintia, Stefano Cresci, Angelo Facchini, Fosca Giannotti, Aristides Gionis, Riccardo Guidotti, Michael Mathioudakis, Cristina Ioana Muntean, Luca Pappalardo, Dino Pedreschi, Evangelos Pournaras, Francesca Pratesi, Maurizio Tesconi, Roberto Trasarti
Summary: The exponential growth of large-scale mobility data has led to the vision of smart cities but also raised privacy concerns. Research communities and industrial stakeholders show strong interest in building knowledge discovery pipelines over these data sources.
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS
(2021)
Proceedings Paper
Computer Science, Information Systems
Aristides Gionis, Antonis Matakos, Bruno Ordozgoiti, Han Xiao
WWW'20: COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2020
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Ryuta Matsuno, Aristides Gionis
PROCEEDINGS OF THE 2020 SIAM INTERNATIONAL CONFERENCE ON DATA MINING (SDM)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Suhas Thejaswi, Aristides Gionis
PROCEEDINGS OF THE 2020 SIAM INTERNATIONAL CONFERENCE ON DATA MINING (SDM)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Guangyi Zhang, Aristides Gionis
PROCEEDINGS OF THE 2020 SIAM INTERNATIONAL CONFERENCE ON DATA MINING (SDM)
(2020)
Proceedings Paper
Computer Science, Information Systems
Han Xiao, Bruno Ordozgoiti, Aristides Gionis
WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020)
(2020)
Proceedings Paper
Computer Science, Information Systems
Bruno Ordozgoiti, Antonis Matakos, Aristides Gionis
WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020)
(2020)