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Autore: Grafiati

Pubblicato: 1 giugno 2024

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1

Loukides, Grigorios, e Jian-Hua Shao. "An Efficient Clustering Algorithm for k-Anonymisation". Journal of Computer Science and Technology 23, n. 2 (marzo 2008): 188–202. http://dx.doi.org/10.1007/s11390-008-9121-3.

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2

Natwichai, Juggapong, Xue Li e Asanee Kawtrkul. "Incremental processing and indexing for (k, e)-anonymisation". International Journal of Information and Computer Security 5, n. 3 (2013): 151. http://dx.doi.org/10.1504/ijics.2013.055836.

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3

Stark, Konrad, Johann Eder e Kurt Zatloukal. "Achieving k-anonymity in DataMarts used for gene expressions exploitation". Journal of Integrative Bioinformatics 4, n. 1 (1 marzo 2007): 132–44. http://dx.doi.org/10.1515/jib-2007-58.

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Abstract Gene expression profiling is a sophisticated method to discover differences in activation patterns of genes between different patient collectives. By reasonably defining patient groups from a medical point of view, subsequent gene expression analysis may reveal disease-related gene expression patterns that are applicable for tumor markers and pharmacological target identification. When releasing patient-specific data for medical studies privacy protection has to be guaranteed for ethical and legal reasons. k-anonymisation may be used to generate a sufficient number of k data twins in order to ensure that sensitive data used in analyses is protected from being linked to individuals. We use an adapted concept of k-anonymity for distributed data sources and include various customisation parameters in the anonymisation process to guarantee that the transformed data is still applicable for further processing. We present a real-world medical-relevant use case and show how the related data is materialised, anonymised, and released in a data mart for testing the related hypotheses.

4

de Haro-Olmo, Francisco José, Ángel Jesús Varela-Vaca e José Antonio Álvarez-Bermejo. "Blockchain from the Perspective of Privacy and Anonymisation: A Systematic Literature Review". Sensors 20, n. 24 (14 dicembre 2020): 7171. http://dx.doi.org/10.3390/s20247171.

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The research presented aims to investigate the relationship between privacy and anonymisation in blockchain technologies on different fields of application. The study is carried out through a systematic literature review in different databases, obtaining in a first phase of selection 199 publications, of which 28 were selected for data extraction. The results obtained provide a strong relationship between privacy and anonymisation in most of the fields of application of blockchain, as well as a description of the techniques used for this purpose, such as Ring Signature, homomorphic encryption, k-anonymity or data obfuscation. Among the literature researched, some limitations and future lines of research on issues close to blockchain technology in the different fields of application can be detected. As conclusion, we extract the different degrees of application of privacy according to the mechanisms used and different techniques for the implementation of anonymisation, being one of the risks for privacy the traceability of the operations.

5

Zhang, Yuliang, Tinghuai Ma, Jie Cao e Meili Tang. "K-anonymisation of social network by vertex and edge modification". International Journal of Embedded Systems 8, n. 2/3 (2016): 206. http://dx.doi.org/10.1504/ijes.2016.076114.

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6

Ganabathi, G. Chitra, e P. Uma Maheswari. "Efficient clustering technique for k-anonymisation with aid of optimal KFCM". International Journal of Business Intelligence and Data Mining 15, n. 4 (2019): 430. http://dx.doi.org/10.1504/ijbidm.2019.102809.

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7

Singh, Amardeep, Monika Singh, Divya Bansal e Sanjeev Sofat. "Optimised K-anonymisation technique to deal with mutual friends and degree attacks". International Journal of Information and Computer Security 14, n. 3/4 (2021): 281. http://dx.doi.org/10.1504/ijics.2021.114706.

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8

Sofat, Sanjeev, Divya Bansal, Monika Singh e Amardeep Singh. "Optimised K-anonymisation technique to deal with mutual friends and degree attacks". International Journal of Information and Computer Security 14, n. 3/4 (2021): 281. http://dx.doi.org/10.1504/ijics.2021.10037248.

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9

Yaji, Sharath, e B. Neelima. "Parallel computing for preserving privacy using k-anonymisation algorithms from big data". International Journal of Big Data Intelligence 5, n. 3 (2018): 191. http://dx.doi.org/10.1504/ijbdi.2018.092659.

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10

Yaji, Sharath, e B. Neelima.B. "Parallel computing for preserving privacy using k-anonymisation algorithms from big data". International Journal of Big Data Intelligence 5, n. 3 (2018): 191. http://dx.doi.org/10.1504/ijbdi.2018.10008733.

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11

Matet, Benoit, Angelo Furno, Marco Fiore, Etienne Côme e Latifa Oukhellou. "Adaptative generalisation over a value hierarchy for the k-anonymisation of Origin–Destination matrices". Transportation Research Part C: Emerging Technologies 154 (settembre 2023): 104236. http://dx.doi.org/10.1016/j.trc.2023.104236.

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12

Solanki, Paresh, Sanjay Garg e Hitesh Chhinkaniwala. "Heuristic-based hybrid privacy-preserving data stream mining approach using SD-perturbation and multi-iterative k-anonymisation". International Journal of Knowledge Engineering and Data Mining 5, n. 4 (2018): 306. http://dx.doi.org/10.1504/ijkedm.2018.095522.

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13

Madan, Suman, e Puneet Goswami. "A privacy preservation model for big data in map-reduced framework based on k-anonymisation and swarm-based algorithms". International Journal of Intelligent Engineering Informatics 8, n. 1 (2020): 38. http://dx.doi.org/10.1504/ijiei.2020.10027094.

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14

Madan, Suman, e Puneet Goswami. "A privacy preservation model for big data in map-reduced framework based on k-anonymisation and swarm-based algorithms". International Journal of Intelligent Engineering Informatics 8, n. 1 (2020): 38. http://dx.doi.org/10.1504/ijiei.2020.105433.

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15

Bartholomäus, Sebastian, Yannik Siegert, Hans Werner Hense e Oliver Heidinger. "Secure Linking of Data from Population-Based Cancer Registries with Healthcare Data to Evaluate Screening Programs". Das Gesundheitswesen 82, S 02 (10 dicembre 2019): S131—S138. http://dx.doi.org/10.1055/a-1031-9526.

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Abstract Background The evaluation of population-based screening programs, like the German Mammography Screening Program (MSP), requires collection and linking data from population-based cancer registries and other sources of the healthcare system on a case- specific level. To link such sensitive data, we developed a method that is compliant with German data protection regulations and does not require written individual consent. Methods Our method combines a probabilistic record linkage on encrypted identifying data with ‘blinded anonymisation’. It ensures that all data either are encrypted or have a defined and measurable degree of anonymity. The data sources use a software to transform plain-text identifying data into a set of irreversibly encrypted person cryptograms, while the evaluation attributes are aggregated in multiple stages and are reversibly encrypted. A pseudonymisation service encrypts the person cryptograms into record assignment numbers and a downstream data-collecting centre uses them to perform the probabilistic record linkage. The blinded anonymisation solves the problem of quasi-identifiers within the evaluation data. It allows selecting a specific set of the encrypted aggregations to produce data export with ensured k-anonymity, without any plain-text information. These data are finally transferred to an evaluation centre where they are decrypted and analysed. Our approach allows creating several such generalisations, with different resulting suppression rates allowing dynamic balance information depth with privacy protection and also highlights how this affects data analysability. Results German data protection authorities approved our concept for the evaluation of the impact of the German MSP on breast cancer mortality. We implemented a prototype and tested it with 1.5 million simulated records, containing realistically distributed identifying data, calculated different generalisations and the respective suppression rates. Here, we also discuss limitations for large data sets in the cancer registry domain, as well as approaches for further improvements like l-diversity and how to reduce the amount of manual post-processing. Conclusion Our approach enables secure linking of data from population-based cancer registries and other sources of the healthcare system. Despite some limitations, it enables evaluation of the German MSP program and can be generalised to be applicable to other projects.

16

Mauw, Sjouke, Yunior Ramírez-Cruz e Rolando Trujillo-Rasua. "Preventing active re-identification attacks on social graphs via sybil subgraph obfuscation". Knowledge and Information Systems 64, n. 4 (27 febbraio 2022): 1077–100. http://dx.doi.org/10.1007/s10115-022-01662-z.

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AbstractActive re-identification attacks constitute a serious threat to privacy-preserving social graph publication, because of the ability of active adversaries to leverage fake accounts, a.k.a. sybil nodes, to enforce structural patterns that can be used to re-identify their victims on anonymised graphs. Several formal privacy properties have been enunciated with the purpose of characterising the resistance of a graph against active attacks. However, anonymisation methods devised on the basis of these properties have so far been able to address only restricted special cases, where the adversaries are assumed to leverage a very small number of sybil nodes. In this paper, we present a new probabilistic interpretation of active re-identification attacks on social graphs. Unlike the aforementioned privacy properties, which model the protection from active adversaries as the task of making victim nodes indistinguishable in terms of their fingerprints with respect to all potential attackers, our new formulation introduces a more complete view, where the attack is countered by jointly preventing the attacker from retrieving the set of sybil nodes, and from using these sybil nodes for re-identifying the victims. Under the new formulation, we show that k-symmetry, a privacy property introduced in the context of passive attacks, provides a sufficient condition for the protection against active re-identification attacks leveraging an arbitrary number of sybil nodes. Moreover, we show that the algorithm K-Match, originally devised for efficiently enforcing the related notion of k-automorphism, also guarantees k-symmetry. Empirical results on real-life and synthetic graphs demonstrate that our formulation allows, for the first time, to publish anonymised social graphs (with formal privacy guarantees) that effectively resist the strongest active re-identification attack reported in the literature, even when it leverages a large number of sybil nodes.

17

Matet, Benoit, Angelo Furno, Marco Fiore, Etienne Come e Latifa Oukhellou. "Adaptative Generalisation Over a Value Hierarchy for the K-Anonymisation of Origin-Destination Matrices". SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4273504.

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18

Ren, Wang, Xin Tong, Jing Du, Na Wang, Shancang Li, Geyong Min e Zhiwei Zhao. "Privacy Enhancing Techniques in the Internet of Things Using Data Anonymisation". Information Systems Frontiers, 11 maggio 2021. http://dx.doi.org/10.1007/s10796-021-10116-w.

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AbstractThe Internet of Things (IoT) and Industrial 4.0 bring enormous potential benefits by enabling highly customised services and applications, which create huge volume and variety of data. However, preserving the privacy in IoT and Industrial 4.0 against re-identification attacks is very challenging. In this work, we considered three main data types generated in IoT: context data, continuous data, and media data. We first proposed a stream data anonymisation method based on k-anonymity for data collected by IoT devices; and then privacy enhancing techniques for both continuous data and media data were proposed for different IoT scenarios. The experiment results show that the proposed techniques can well preserve privacy without significantly affecting the utility of the data.

19

"Preservation of Privacy using Multidimensional K-Anonymity Method for Non-Relational Data". International Journal of Recent Technology and Engineering 8, n. 2S10 (11 ottobre 2019): 544–47. http://dx.doi.org/10.35940/ijrte.b1096.0982s1019.

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Mining of huge data having complexity is a challenging issue also maintaining Privacy of data is also equally important ,sometimes there is a need to release data for use of researchers or for the purpose of gaining knowledge or earn money this release of data includes releas e of all attributes of personal data. when this type of data like Insurance record data, Medical diagnosis data, funding scheme data is release even if we remove sensitive attribute like Name for hiding personal details still data re-identification is possible by linking public data like voters data with these released data and by linking the quasi identifiers we are able to get sensitive information about person like critical disease, financial position etc. by applying k–Anonymization using multiple dimensions of attributes we are able to hide these sensitive attributes by generalising and suppressing the Quasi identifiers so that when linking with public database is done no records are re-identified, also we obtained results for quality measures for anonymisation and observed that the value of k once we start increase after some threshold anonymity starts decreasing so there is a need to choose proper value of k on non-relational data

20

Lu, Yang. "Semantic-based Privacy-preserving Record Linkage." International Journal of Population Data Science 7, n. 3 (25 agosto 2022). http://dx.doi.org/10.23889/ijpds.v7i3.1956.

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IntroductionSharing aggregated electronic health records (EHRs) for integrated health care and public health studies is increasingly demanded. Patient privacy demands that anonymisation procedures are in place for data sharing. ObjectiveTraditional methods such as k-anonymity and its derivations are often overgeneralising resulting in lower data accuracy. To tackle this issue, we proposed the Semantic Linkage K-Anonymity (SLKA) approach to balance the privacy and utility preservation through detecting risky combinations hidden in the record linkage releases. ApproachK-anonymity processing quasi-identifiers of data may lead to ‘over generalisation’ when dealing with linkage data sets. As most linkage cases do not include all local patients and thus not all modifying data for privacy-preserving purposes needs to be used, we proposed the linkage k-anonymity (LKA) by which only obfuscated individuals in a released linkage set are required to be indistinguishable from at least k-1 other individuals in the local dataset. Considering the inference disclosure issue, we further designed the semantic-based linkage k-anonymity (SLKA) method through extending with a semantic-rule base for automatic detection of (and ruling out) risky associations from previous linked data releases. Specially, associations identified from the “previous releases” of the linkage dataset can become the input of semantic reasoning for the “next release”. ResultsThe approach is evaluated based on a linkage scenario where researchers apply to link data from an Australia-wide national type-1 diabetes platform with survey results from 25,000+ Victorians about their health and wellbeing. In comparing the information loss of three methods, we find that extra cost can be incurred in SLKA for dealing with risky individuals, e.g., 13.7% vs 5.9% (LKA, k=4) however it performs much better than k-anonymity, which can cause 24% information loss (k=4). Besides, the k values can affect the level of distortion in SLKA, such as 11.5% (k=2) vs 12.9% (k=3). ConclusionThe SLKA framework provides dynamic protection for repeated linkage releases while preserving data utility by avoiding unnecessary generalisation as typified by k-anonymity.

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Avraam, Demetris, Elinor Jones e Paul Burton. "A deterministic approach for protecting privacy in sensitive personal data". BMC Medical Informatics and Decision Making 22, n. 1 (28 gennaio 2022). http://dx.doi.org/10.1186/s12911-022-01754-4.

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Abstract Background Data privacy is one of the biggest challenges for any organisation which processes personal data, especially in the area of medical research where data include sensitive information about patients and study participants. Sharing of data is therefore problematic, which is at odds with the principle of open data that is so important to the advancement of society and science. Several statistical methods and computational tools have been developed to help data custodians and analysts overcome this challenge. Methods In this paper, we propose a new deterministic approach for anonymising personal data. The method stratifies the underlying data by the categorical variables and re-distributes the continuous variables through a k nearest neighbours based algorithm. Results We demonstrate the use of the deterministic anonymisation on real data, including data from a sample of Titanic passengers, and data from participants in the 1958 Birth Cohort. Conclusions The proposed procedure makes data re-identification difficult while minimising the loss of utility (by preserving the spatial properties of the underlying data); the latter means that informative statistical analysis can still be conducted.

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O’Keefe, Christine M. "Privacy, Governance and Public Acceptability in Population Data Linkage for Research". International Journal of Population Data Science 1, n. 1 (12 aprile 2017). http://dx.doi.org/10.23889/ijpds.v1i1.405.

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ABSTRACT IntroductionFor several years, Population Data Linkage initiatives around the world have been successfully linking population‐based administrative and other datasets and making extracts available for research under strong confidentiality protections1. This paper provides an overview of current approaches in a range of scenarios, then outlines current relevant trends and potential implications for population data linkage initiatives.MethodsApproaches to protecting the confidentiality of data in research can also reduce the statistical usefulness, and the trade‐off between confidentiality protection and statistical usefulness is often represented as a Risk‐Utility map [2, 3, 5, 7]. Positioning the range of current approaches on such a Risk‐Utility map can indicate the relative nature of the trade‐off in each case.Such a Risk‐Utility map is only part of the story, however. Each approach needs to be implemented with appropriate levels of governance, information technology security, and ethical oversight. In addition, there are several changes in the external environment that have potential implications for population data linkage initiatives.Results and DiscussionCurrent approaches to protecting the confidentiality of data in research fall into one of two classes. The first class comprises approaches that anonymise the data before analysis, namely: Removal of identifying information such as names and addresses Secure data centres on‐site at the custodian premises Public use files made widely available Synthetic data files made widely available Open data files published on the internet The second class comprises approaches that anonymise the analysis outputs, namely: Virtual data centres that are on‐line versions of secure data centres [8] Remote analysis centres where users can request analyses but cannot see data. Many such initiatives implicitly or explicitly use criteria that have been recently captured in the Five Safes model [3]. However, changes in the external environment may add potential implications to address [6].First, there is a rapid increase in scenarios for data use, many of which involve multiple datasets from multiple sources with multiple custodians. This raises the question of whether there should be centralised data integration versus a proliferation of ad‐hoc decentralised but inter‐related initiatives. In any case, harmonised and shared governance will be essential. Next, the public are becoming increasingly informed and are increasingly exercising their privacy preferences in selecting between competing service providers. It is likely that the public will demand that initiatives move beyond education gain acceptance to a model of full partnership.ConclusionsWhile Population Data Linkage initiatives have been successful to date, changes in the external environment have potential implications such as a need for harmonised and shared governance, as well as full partnership with the public. Meeting the future challenges will require sophistication in the selection, design and operation of approaches to protecting the confidentiality of data in research. Useful frameworks in this context include [1, 4]. Importantly, it is necessary to have a range of approaches in order to adequately meet the needs of a range of different scenarios.AcknowledgementsThis work was partially supported by a grant from the Simons Foundation. The author thanks the Isaac Newton Institute for Mathematical Sciences, University of Cambridge, for support and hospitality during the programme Data Linkage and Anonymisation, which was supported by EPSRC grant no EP/K032208/1. 1For a list of administrative data linkage centres around the world, see www.ipdln.org/data‐linkage‐centres Key References[1] Desai T, Felix Ritchie F, Welpton R. Five safes: designing data access for research. Preprint 2016.[2] Duncan G, Elliot M, Salazar‐Gonzàlez JJ. Statistical Confidentiality. Springer: New York, 2011.[3] El Emam K. A Guide to the De‐identification of Health Information. CRC Press: New York, NY, 2013.[4] Elliot M, Mackey E, O’Hara K, Tudor C. The Anonymisation Decision‐Making Framework. http://ukanon.net/wp‐content/uploads/2015/05/The‐Anonymisation‐Decision‐making‐Framework.pdf[5] Hundepool A, Domingo‐Ferrer J, Franconi L, Giessing S, Nordholt E, Spicer K, deWolf PP. Statistical Disclosure Control, Wiley Series in Survey Methodology. John Wiley & Sons: United Kingdom, 2012.[6] O’Keefe CM, Gould P, Chipperfield JO. A Five Safes perspective on administrative data integration initiatives, submitted.[7] O'Keefe CM and Rubin DB. Individual Privacy versus Public Good: Protecting Confidentiality in Health Research, Statistics in Medicine 34 (2015), 3081‐3103. DOI: 10.1002/sim.6543[8] O’Keefe CM, Westcott M, O’Sullivan M, Ickowicz A, Churches T. Anonymization for outputs of population health and health services research conducted via an online data centre, JAMIA in press.