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Extended K-Anonymity Model for Privacy Preserving on Micro Data

Masoud Rahimi, Mehdi Bateni, Hosein Mohammadinejad
2015 International Journal of Computer Network and Information Security  
In order to overcome this problem, several approaches, called p-sensitive k-anonymity, p+-sensitive k-anonymity, and (p, α)-sensitive k-anonymity, were proposed.  ...  The drawbacks of these methods include the inability to protect micro datasets against attribute disclosure and the high value of the distortion ratio.  ...  A.3 -The Distortion Ratio: the distortion ratio of four privacy preservation measures were compared: psensitive k-anonymity, p+-sensitive k-anonymity, (p, α)sensitive k-anonymity, (k, p, t) Anonymizing3Layer  ... 
doi:10.5815/ijcnis.2015.12.05 fatcat:2mk4qoegebfpbj6pl3lilg6aki

State-of-the-art in Privacy Preserved K-anonymity Revisited

Yousra Abdul Alsahib S. Aldeen, Mazleena Salleh
2016 Research Journal of Applied Sciences Engineering and Technology  
Moreover, this study is grounded on the fundamental ideas and concepts of the existing K-anonymity privacy preservation, K-anonymity model and enhanced the K-anonymity model.  ...  In addressing this issue, K-anonymity is amongst the most reliable and valid algorithms used for privacy preservation in data mining.  ...  It analyzes the central premises, ideas, models of existing K-anonymity algorithm and highlights the future trends of privacy protection facilitated K-anonymity development.  ... 
doi:10.19026/rjaset.12.2753 fatcat:okglfolrtzcg7gk6zont3qeaau

Utility-driven k-anonymization of public transport user data

Bhawani Shanker Bhati, Jordan Ivanchev, Iva Bojic, Anwitaman Datta, David Eckhoff
2021 IEEE Access  
INDEX TERMS Clustering, k-anonymity, privacy, tap-in tap-out transportation data, utility.  ...  In this article, we propose a k-anonymity approach that prioritizes the generalization of attributes based on their utility.  ...  While this assumption improves privacy protection by eliminating some of the common weaknesses of k-anonymity, it will negatively affect utility as generalization and suppression will now incorporate all  ... 
doi:10.1109/access.2021.3055505 fatcat:ntd4mbew4nexdbh4mmpog6fut4

Improving data utility in differential privacy and k-anonymity [article]

Jordi Soria-Comas
2013 arXiv   pre-print
We focus on two mainstream privacy models: k-anonymity and differential privacy. Once a privacy model has been selected, the goal is to enforce it while preserving as much data utility as possible.  ...  On the data utility side, dealing with a large number of quasi-identifier attributes is problematic. We propose a relaxation of k-anonymity that deals with these issues.  ...  Probabilistic k-anonymity We propose a privacy model that, similarly to k-anonymity, protects against identity disclosure (the probability of determining the true identity for a specific value of a confidential  ... 
arXiv:1307.0966v1 fatcat:adgnr7mbirbatkeirelc7pj67a

k-Anonymization in the Presence of Publisher Preferences

Rinku Dewri, Indrajit Ray, Indrakshi Ray, Darrell Whitley
2011 IEEE Transactions on Knowledge and Data Engineering  
Existing methods for this purpose modify the data only to the extent necessary to satisfy the privacy constraints, thereby asserting that the information loss has been minimized.  ...  Privacy constraints are typically enforced on shared data that contain sensitive personal attributes.  ...  This check must be appropriately modified when considering a privacy model different from k-anonymity.  ... 
doi:10.1109/tkde.2011.106 fatcat:nvakv5dvirexjflk4wdvwaspwi

Towards Attack and Defense Views to k-anonymous using Information Theory Approach

Cheng Liu, Youliang Tian, Jinbo Xiong, Yanhua Lu, Qiuxian Li, Changgen Peng
2019 IEEE Access  
In this paper, we give a quantitative method for the capability of attacker and defender under k-anonymous model.  ...  Ensuring multimedia content security is one of the main targets to solve multimedia information security problems and k-anonymous model is a classic and effective model to protect personal information  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.  ... 
doi:10.1109/access.2019.2947233 fatcat:c3dhwjohgvflte6r5n6kftbauy

Improved k-Anonymize and l-Diverse Approach for Privacy Preserving Big Data Publishing Using MPSEC Dataset

Priyank Jain, Manasi Gyanchandani, Nilay Khare
2020 Computing and informatics  
Then IKA is further categorized into Improved Symmetric k-Anonymization (ISKA) and Improved Asymmetric k-Anonymization (IAKA). After anonymizing data using IKA, ILD model is used to increase privacy.  ...  In order to maintain privacy, different methods of k-anonymization and l-diversity have been widely used. But for larger datasets, the results are not very promising.  ...  Acknowledgement We are grateful to the Madhya Pradesh State Election Commission, India, for their enthusiastic and constant support and for providing us with the real-time big dataset needed for the research  ... 
doi:10.31577/cai_2020_3_537 fatcat:rd6fhxpnxvh2rd5qojdukmus2i

Data Security and Privacy-Preserving Framework Using Machine Learning and Blockchain in Big-Data to Data Middle Platform in the Era of IR 4.0 [chapter]

Chen Chuqiao, S.B. Goyal
2021 Advances in Parallel Computing  
Based on these gaps, trying to create a suitable framework to guide the industry to protect their privacy when the organization contribute and operate their data middle platform.  ...  The traditional privacy-preventing model is not enough, we need the help of Machine-Learning and the Blockchain.  ...  [14] who is working at the Cornell University noticed that the k-anonymity model has a weak point. Even though there is nothing be restricted on sensitive attributes.  ... 
doi:10.3233/apc210190 fatcat:7j5qgemtizbyroemvmodgje4ju

A comprehensive review on privacy preserving data mining

Yousra Abdul Alsahib S. Aldeen, Mazleena Salleh, Mohammad Abdur Razzaque
2015 SpringerPlus  
, distributed, and k-anonymity, where their notable advantages and disadvantages are emphasized.  ...  Further significant enhancements for more robust privacy protection and preservation are affirmed to be mandatory.  ...  Acknowledgements Authors are thankful to UTM library for providing several useful references. Competing interests The authors declare that they have no competing interests.  ... 
doi:10.1186/s40064-015-1481-x pmid:26587362 pmcid:PMC4643068 fatcat:twwirrmehva4pfkieiufldrxve

Perturbation Privacy for Sensitive Locations in Transit Data Publication: A Case Study of Montreal Trajet Surveys [article]

Godwin Badu-Marfo, Bilal Farooq, Zachary Patterson
2019 arXiv   pre-print
At the same time, in order for such data to be useful, as much spatial resolution as possible is desirable for utility in transportation applications and travel demand modeling.  ...  Geo-indistinguishability and the Donut geomask) in protecting the privacy of respondents whose residential location may be published.  ...  K-Anonymity K-Anonymity is the most widely used class of privacy protection technique for location-based systems existing in literature.  ... 
arXiv:1901.07155v1 fatcat:35r3otl7knhx5mrnbkqxcsy3iq

Vulnerability- and Diversity-Aware Anonymization of Personally Identifiable Information for Improving User Privacy and Utility of Publishing Data

Abdul Majeed, Farman Ullah, Sungchang Lee
2017 Sensors  
Differential privacy (DP) [7] has emerged as a state-of-the-art method and one of the most promising privacy models for releasing person-specific data in interactive settings.  ...  This paper proposes a new anonymization scheme that considers the identity vulnerability of PII to effectively protect user privacy.  ...  The k-anonymity [18] is a well-known PPDP technique that protects user privacy by introducing k-users with the same QIs in each equivalence class.  ... 
doi:10.3390/s17051059 pmid:28481298 pmcid:PMC5469664 fatcat:se4kgoicajhbtgjpk3ha4khzhe

Privacy Preserving Large-Scale Rating Data Publishing

Xiaoxun Sun, Lili Sun
2013 EAI Endorsed Transactions on Scalable Information Systems  
Recently, a new privacy concern has emerged in privacy preservation research: how to protect individuals' privacy in large survey rating data.  ...  In order to protect the privacy in the large-scale rating data, it is important to propose new privacy principles which consider the properties of the rating data.  ...  The first one is (k, )-anonymity model, which targets at protecting individual's identity and the second model is (k, , l)-anonymity model, which not only protects individual's identity, but also the personal  ... 
doi:10.4108/trans.sis.2013.01-03.e3 fatcat:wfppunsl7ra5jpfw7ql4jvwr4u

Privacy in Preprocessing of Healthcare Data

Mukesh Soni1 *, Yashkumar Barot2 and S. Gomathi3
2021 Zenodo  
The L-diversity model maintains some of the weakness in the k-anonymous model, in that the preserved identity is not equivalent to protecting sensitive feelings that are normalized or suppressed to the  ...  However, this leads to data distortion and therefore more and more information is lost due to K-anonymity.  ...  Apart from this when we have such a good system but still they are not sufficient for healthcare data mining.  ... 
doi:10.5281/zenodo.5171447 fatcat:t53b3u2ypncutcyp2u6d6yiwda

Security and Accuracy Constrained Task-Role based Access Control and Privacy Preserving Mechanism for Relational Data

Pratik Bhingardeve, Prof. D. H. Kulkarni
2015 International Journal of Engineering Research and  
PPM and ACM with task-role based access to provide high security and privacy for our relational data.  ...  During a time where the moment subtle elements of our life are recorded and put away in databases, a reasonable Catch is developing between the need to protect the security of people and the need to utilize  ...  The l-diversity model handles a number of the weaknesses within the k-anonymity model wherever protected identities to the extent of k-individuals isn't like protective the corresponding sensitive values  ... 
doi:10.17577/ijertv4is070918 fatcat:2ucktco7ofeo5gpzbzppfbd57q

Privacy-preserving data publishing

Benjamin C. M. Fung, Ke Wang, Rui Chen, Philip S. Yu
2010 ACM Computing Surveys  
ACKNOWLEDGMENTS We sincerely thank the reviewers of this manuscript for greatly improving the quality of this survey.  ...  proposed a privacy model called MultiR k-anonymity to ensure k-anonymity on multiple relational tables.  ...  I Privacy Models Attack Model Privacy Model Record Linkage Attribute Linkage Table Linkage Probabilistic Attack k-Anonymity MultiR k-Anonymity -Diversity Confidence Bounding (α, k)-Anonymity  ... 
doi:10.1145/1749603.1749605 fatcat:ivbwule7bjafvmdvh236gbwryu
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