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Enhancing efficiency and data utility in longitudinal data anonymization

INFORMATION SCIENCES 2025年 704卷

作者： Amiri, Fatemeh Sanchez, David Domingo-Ferrer, Josep Hamedan Univ Technol Dept Comp Engn Hamadan Iran Univ Rovira & Virgili CYBERCAT Ctr Cybersecur Res Catalonia Dept Comp Engn & Math Ave Paisos Catalans 26 E-43007 Tarragona Catalonia Spain

Longitudinal data consist of observations collected over time from a set of individuals. The accumulation of information on each individual over time makes longitudinal data particularly privacy-sensitive. However, existing anonymization methods are often inadequate for ensuring privacy-preserving publication of such data, as current privacy models assume unrealistic levels of attacker knowledge. To address this, we propose the (k, beta)L-privacy model, which assumes that an attacker's knowledge is limited to a subsequence of L quasi-identifiers. This provides a more realistic representation of the information an attacker might actually possess. Our model guarantees that every subsequence of L quasi-identifier values appears in either zero or at least k records within the longitudinal database. Additionally, it ensures that the confidence of any sensitive value within these k records is at most beta times higher than its confidence in the entire dataset. This not only strengthens privacy protection but also enhances data utility. Furthermore, we introduce FCLA, an anonymization algorithm designed to enforce our privacy model while prioritizing data utility. FCLA effectively mitigates identity and attribute disclosures, as well as skewness attacks in longitudinal data. It achieves this by partitioning sequences into groups and anonymizing them independently-a process that can be efficiently parallelized. Experimental results show that FCLA outperforms existing methods in preserving data utility while adhering to strict privacy constraints. Additionally, time complexity analysis and execution time measurements demonstrate that FCLA is more efficient and scalable than alternative approaches.

关键词： Longitudinal data publishing Sequence data publishing data anonymization data privacy

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Parallel privacy preservation through partitioning (P4): a scalable data anonymization algorithm for health data

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2025年第1期25卷 1-12页

作者： Halilovic, Mehmed Meurers, Thierry Otte, Karen Prasser, Fabian Charite Univ Med Berlin Berlin Inst Hlth Med Informat Grp Charitepl 1 D-10117 Berlin Germany

BackgroundSharing health data holds great potential for advancing medical research but also poses many challenges, including the need to protect people's privacy. One approach to address this is data anonymization, which refers to the process of altering or transforming a dataset to preserve the privacy of the individuals contributing data. To this, privacy models have been designed to measure risks and optimization algorithms can be used to transform data to achieve a good balance between risks reduction and the preservation of the dataset's utility. However, this process is computationally complex and challenging to apply to large datasets. Previously suggested parallel algorithms have been tailored to specific risk models, utility models and transformation *** present a novel parallel algorithm that supports a wide range of methods for measuring risks, optimizing utility and transforming data. The algorithm trades data utility for parallelization, by anonymizing partitions of the dataset in parallel. To ensure the correctness of the anonymization process, the algorithm carefully controls the process and if needed rearranges partitions and performs additional *** demonstrate the effectiveness of our method through an open-source implementation. Our experiments show that our approach can reduce execution times by up to one order of magnitude with minor impacts on output data utility in a wide range of *** novel P4 algorithm for parallel and distributed data anonymization is, to the best of our knowledge, the first to systematically support a wide variety of privacy, transformation and utility models.

关键词： Privacy data anonymization Parallelization Scalability Utility

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data anonymization evaluation for big data and IoT environment

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INFORMATION SCIENCES 2022年 605卷 381-392页

作者： Ni, Chunchun Cang, Li Shan Gope, Prosanta Min, Geyong Univ Elect Sci Technol China Dept Comp Sci Chengdu Peoples R China Univ Sheffield Dept Comp Sci Sheffield S1 4DP England Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England

The growth of big data can increase risks of re-identification in complex IoT environment. data anonymization is widely used to prevent shared data from being re-identified private or sensitive information from anonymized data with other available data. It is very important to understand the risks of re-identification of results and process of commonly used anonymization techniques before properly mitigating. 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. Specifically, data utility, privacy metrics, and information loss were used to evaluate data anonymization schemes that can further optimise the data anonymization process to keep risks of re identification low. 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. CO 2022 Published by Elsevier Inc.

关键词： data anonymization Privacy model Re-identification Privacy evaluation data utility data Security

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data anonymization With Diversity Constraints

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2023年第4期35卷 3603-3618页

作者： Milani, Mostafa Huang, Yu Chiang, Fei Univ Western Ontario Dept Comp Sci London ON N6A 3K7 Canada McMaster Univ Dept Comp & Software Hamilton ON L8S 4L8 Canada

Recent privacy legislation has aimed to restrict and control the amount of personal data published by companies and shared with third parties. Much of this real data is not only sensitive requiring anonymization but also contains characteristic details from a variety of individuals. This diversity is desirable in many applications ranging from Web search to drug and product development. Unfortunately, data anonymization techniques have largely ignored diversity in its published result. This inadvertently propagates underlying bias in subsequent data analysis. We study the problem of finding a diverse anonymized data instance where diversity is measured via a set of diversity constraints. We formalize diversity constraints, and study their fundamental problems of satisfiability, implication, and validation. We show that determining the existence of a diverse, anonymized instance can be done in PTIME, and we present a clustering-based algorithm, along with optimizations to improve performance. We conduct extensive experiments using real and synthetic data showing the effectiveness of our techniques, and improvement over existing baselines. Our work aligns with recent trends towards responsible data science by coupling diversity with privacy-preserving data publishing.

关键词： Hypertension Loss measurement Blood Publishing Osteoarthritis Knowledge engineering Hospitals data anonymization data diversity diversity constraints

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data anonymization evaluation against re-identification attacks in edge storage

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WIRELESS NETWORKS 2024年第6期30卷 5263-5277页

作者： Chen, Minghui Cang, Li Shan Chang, Zheng Iqbal, Muddesar Almakhles, Dhafer UESTC Sch Comp Sci & Engn Chengdu Peoples R China UESTC Chengdu Peoples R China London South Bank Univ London England Prince Sultan Univ Coll Engn Riyadh 11586 Saudi Arabia

Edge storage is driven by the emerging edge computing and application intelligence, which makes the data anonymization become essential to guarantee the security of the data. The risk with data anonymization is that it can be re-identified, where anonoymized data is matched with available information to discover the individual. Appropriate data anonymization techniques can help on reducing the risk of re-identification. It is very challenging to protect data privacy against re-identification attacks without significantly reduce usefulness of the data. This work proposes an evaluation framework for data anonymization techniques to measure the risks of re-identification and usefulness of anonymized data.

关键词： data anonymization Decision tree Re-identification risks data usefulness

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data anonymization for Requirements Quality Analysis: a Reproducible Automatic Error Detection Task 11

Data Anonymization for Requirements Quality Analysis: a Repr...

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11th International Conference on Language Resources and Evaluation (LREC)

作者： Kang, Juyeon Park, Jungyeul PROMETIL 52 Rue Jacques Babinet F-31100 Toulouse France CONJECTO 74 Rue Paris F-35000 Rennes France

ISBN: (纸本)9791095546009

In this work, we aim at identifying potential problems of ambiguity, completeness, conformity, singularity and readability in system and software requirements specifications. Those problems arise particularly when they are written in a natural language. While we describe them from a linguistic point of view, the business impacts of each potential error are also considered in system engineering context. We investigate and explore error patterns for requirements quality analysis by manually analyzing the corpus. This analysis is based on the requirements grammar that we developed in our previous work. In addition, this paper extends our previous work in a two-fold way: (1) we increase more than twice the number of evaluation data (1K sentences) through a manual verification process, and (2) we anonymize all sensible and confidential entities in evaluation data to make our data publicly available. We also provide the baseline system using conditional random fields for requirements quality analysis, and we obtain 79.47% for the F-1 score on proposed evaluation data.

关键词： Requirements quality analysis (ReQA) error detection in ReQA data anonymization reproducible task

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data anonymization Based on Natural Equivalent Class 23

Data Anonymization Based on Natural Equivalent Class

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IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Guo, Naixuan Yang, Ming Gong, Qiyuan Chen, Zhouguo Luo, Junzhou Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Sci & Technol Commun Secur Lab Chengdu Sichuan Peoples R China

ISBN: (纸本)9781728103501

data anonymization is widely used to preserve the utility of published datasets without compromising privacy. The state-of-the-art data anonymization approaches are mainly single-record-based algorithms. They group similar records together one by one, then form equivalence classes through generalization. However, these algorithms didn't utilize equivalence classes which exist in the raw dataset. In this paper, we propose a new concept named natural equivalent class. It refers to the record set with the same quasi-identifier values naturally existing in the raw dataset. We theoretically prove that the natural equivalent class can effectively reduce the computational complexity of clustering algorithms as well as information loss. Then, we propose a novel clustering-based anonymization algorithm, which tries to cluster records without separating any natural equivalent class. Extensive experiments on real world datasets show that our approach outperforms the previous clustering-based anonymization algorithms in terms of efficiency and data utility.

关键词： natural equivalent class data anonymization privacy preserving k-anonymity clustering

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data anonymization Through Slicing Based on Graph-Based Vertical Partitioning 3rd

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3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI)

作者： Sharma, Kushagra Jayashankar, Aditi Banu, K. Sharmila Tripathy, B. K. VIT Univ Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

ISBN: (纸本)9788132225294;9788132225287

data anonymization is a technique that uses data distortion to preserve privacy of public data to be published. Several data anonymization techniques and principles have been proposed in the past such as k-anonymity, l-diversity, and slicing. Slicing promises to address the drawbacks of the other two anonymization models. Our proposition is the use of a graph-based vertical partitioning algorithm (GBVP) in the process of Slicing instead of the originally proposed Partition Around Medoids (PAM). We will present several arguments that favor GBVP against PAM as a choice for clustering algorithm.

关键词： data anonymization Slicing Attribute partitioning k-medoids Clustering Graph-based vertical partitioning

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From t-closeness to differential privacy and vice versa in data anonymization

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KNOWLEDGE-BASED SYSTEMS 2015年第Jan.期74卷 151-158页

作者： Domingo-Ferrer, Josep Soria-Comas, Jordi Univ Rovira & Virgili Dept Comp Engn & Maths UNESCO Chair Data Privacy E-43007 Tarragona Spain

k-anonymity and epsilon-differential privacy are two mainstream privacy models, the former introduced to anonymize data sets and the latter to limit the knowledge gain that results from including one individual in the data set. Whereas basic k-anonymity only protects against identity disclosure, t-closeness was presented as an extension of k-anonymity that also protects against attribute disclosure. We show here that, if not quite equivalent, t-closeness and epsilon-differential privacy are strongly related to one another when it comes to anonymizing data sets. Specifically, k-anonymity for the quasi-identifiers combined with epsilon-differential privacy for the confidential attributes yields stochastic t-closeness (an extension oft-closeness), with t a function of k and epsilon. Conversely, t-closeness can yield c-differential privacy when t = exp(epsilon/2) and the assumptions made by t-closeness about the prior and posterior views of the data hold. (C) 2014 Elsevier B.V. All rights reserved.

关键词： t-closeness epsilon-differential privacy data anonymization Statistical disclosure control Syntactic anonymization Semantic anonymization

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Privacy and utility preserving data clustering for data anonymization and distribution on Hadoop

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2017年 74卷 393-408页

作者： Nayahi, J. Jesu Vedha Kavitha, V. Anna Univ Dept Comp Sci & Engn Reg Campus Tirunelveli Tirunelveli 627007 Tamil Nadu India Univ Coll Engn Dept Comp Sci & Engn Kancheepuram Campus Kancheepuram 631552 India

data privacy is a stringent need when sharing and processing data on a distributed environment or in Internet of Things. Collaborative privacy-preserving data mining based on secured multiparty computation incur high communication and computational cost. data anonymization is a promising technique in the field of privacy-preserving data mining used to protect the data against identity disclosure. Information loss and common attacks possible on the anonymized data are serious challenges of anonymization. Recently, data anonymization using data mining techniques has showed significant improvement in data utility. Still the existing techniques lack in effective handling of attacks. Hence in this paper, an anonymization algorithm based on clustering and resilient to similarity attack and probabilistic inference attack is proposed. The anonymized data is distributed on Hadoop Distributed File System. The method achieves a better trade-off between privacy and utility. In our work the data utility is measured in terms of accuracy and FMeasure with respect to different classifiers. Experiments show that the accuracy, FMeasure and the execution time of the classification algorithms on the privacy-preserved data sets formed by the proposed clustering algorithms are better than the existing algorithms. (C) 2016 Elsevier B.V. All rights reserved.

关键词： data privacy Clustering data anonymization Hadoop Classification Distributed data

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