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Preserving Utility during Attribute-oriented Data Anonymization Process

Sarah Zouinina, Younès Bennani, Maha Ben-Fares, Nicoleta Rogovschi, Abdelouahid Lyhyaoui
2019 Australian Journal of Intelligent Information Processing Systems  
In this paper, we introduce an Attribute-oriented anonymization technique based on Kernel Density Estimation.  ...  The utility of the anonymized data provided is analysed using separability utility and structural utility followed by a combination of both utilities to quantify the privacy-utility trade-off.  ...  constrained Clustering (CollabLM) and Attribute-oriented anonymization (Attribute oriented).  ... 
dblp:journals/ajiips/ZouininaBBRL19 fatcat:rxsrydufazdphoo57oyhvs42be

Utility Enhancement of Deficient Relational Recordset Anonymization

Kishore Samraj, Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University, Rajesh Appusamy, Ramya Shankar, C.Abdul Hakeem College of Engineering and Technology, C.Abdul Hakeem College of Engineering and Technology
2018 International Journal of Intelligent Engineering and Systems  
(CoA), Attribute oriented Anonymization (AoA) and Record oriented Anonymization (RoA) than the existing procedures.  ...  Privacy preserving data mining (PPDM) is the current developing area of research that precisely ensures a certain level of privacy by increasing the utility of information.  ...  During data mining, an individual's data may get revealed by joining the one/more records utilized for the above practises with multiple publicly available data sources.  ... 
doi:10.22266/ijies2018.1231.14 fatcat:nevngzctf5danffgtndmeujqna

A Two-Levels Data Anonymization Approach [chapter]

Sarah Zouinina, Younès Bennani, Nicoleta Rogovschi, Abdelouahid Lyhyaoui
2020 IFIP Advances in Information and Communication Technology  
The results show that the proposed approaches give good results in terms of utility what preserves the trade-off between data privacy and its usefulness.  ...  on the quality of the anonymized data.  ...  Attribute Oriented Kernel Density Estimation for Data Anonymization Another method that we proposed to anonymize a dataset was the Attribute Oriented Kernel Density Estimation [20] .  ... 
doi:10.1007/978-3-030-49161-1_8 fatcat:7bfyztbueng5ngduvcg2seeblq

Design of a Privacy-Preserving Data Platform for Collaboration Against Human Trafficking [article]

Darren Edge, Weiwei Yang, Kate Lytvynets, Harry Cook, Claire Galez-Davis, Hannah Darnton, Christopher M. White
2020 arXiv   pre-print
We present new methods to anonymize, publish, and explore such data, implemented as a pipeline generating three artifacts: (1) synthetic data mitigating the privacy risk that published attribute combinations  ...  might be linked to known individuals or groups; (2) aggregate data mitigating the utility risk that synthetic data might misrepresent statistics needed for official reporting; and (3) visual analytics  ...  CTDC currently ensures that data is anonymized through k-anonymization. However, the process to do so results in the loss of much potentially useful and crucial data.  ... 
arXiv:2005.05688v2 fatcat:ep72y2ehnfdfdepffrlikfccaa

Ontology-Enhanced Interactive Anonymization in Domain-Driven Data Mining Outsourcing

Brian C.S. Loh, Patrick H.H. Then
2010 2010 Second International Symposium on Data, Privacy, and E-Commerce  
Finally, utilizing a correlation-based measure can improve attribute selection during anonymization for domain-driven purposes. Conclusion.  ...  Moreover, previous utility metrics have not considered attribute correlations during generalization. Objective.  ...  During the anonymization process, a utility measure is employed to guide the algorithm towards transformations that provide the best utility and privacy trade-offs.  ... 
doi:10.1109/isdpe.2010.7 fatcat:euaht3j43rcoxkmaqda6bjsqi4

Signal Processing Oriented Approach for Big Data Privacy

Xiaohua Li, Thomas Yang
2015 2015 IEEE 16th International Symposium on High Assurance Systems Engineering  
This paper addresses the challenge of big data security by exploiting signal processing theories.  ...  The utility of the scrambled data is maintained, as demonstrated by a cyber-physical system application.  ...  To guarantee privacy, data must be processed by more advanced anonymization techniques, such as perturbation and k-anonymity.  ... 
doi:10.1109/hase.2015.23 dblp:conf/hase/LiY15 fatcat:vehsyjty2bawboivhy4awhdwou

Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes

J. Jayapradha, M. Prakash, Youseef Alotaibi, Osamah Ibrahim Khalaf, Saleh Ahmed Alghamdi
2022 IEEE Access  
Currently, significant focus has been established to protect privacy during data publishing.  ...  An efficient model Heap Bucketization-anonymity (HBA) has been proposed to balance privacy and utility with multiple sensitive attributes.  ...  Due to the slicing of highly correlated attributes, the data utility is highly preserved in the sensitive attribute table and the quasi-identifier table.  ... 
doi:10.1109/access.2022.3158312 fatcat:dczviwjavnh6pmtklaz5udhp7m

Data Publishing Techniques and Privacy Preserving

Ajit Singh
2019 International Journal for Information Security Research  
Healthcare Data Publishing is the process where certain transformation (such as anonymization, generalization, suppression etc.) can be applied before publishing healthcare data online.  ...  This review paper studies all existing Privacy Preserving Data Publishing (PPDP) schemes using data generalization.  ...  There are certain privacy preserving techniques such as data anonymization compromise data utility to certain extent.  ... 
doi:10.20533/ijisr.2042.4639.2019.0101 fatcat:gdrm2a6lkrgkxezclakdoiazfe

Towards an Efficient Log Data Protection in Software Systems through Data Minimization and Anonymization

A. Omar Portillo-Dominguez, Vanessa Ayala-Rivera
2019 2019 7th International Conference in Software Engineering Research and Innovation (CONISOFT)  
To facilitate this process, log data management is often outsourced to cloud providers.  ...  Our results show that atomic anonymization operations can be effectively applied to log streams to preserve the confidentiality of information, while still allowing to conduct different types of analysis  ...  Typically, this form of anonymization would be used when sensitive attributes are not required for data analysis • Prefix-Preserving.  ... 
doi:10.1109/conisoft.2019.00024 fatcat:goeebqn23za6joulk5avoom2ay

D2D Big Data Privacy-Preserving Framework Based on (a, k)-Anonymity Model

Jie Wang, Hongtao Li, Feng Guo, Wenyin Zhang, Yifeng Cui
2019 Mathematical Problems in Engineering  
In this paper, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce.  ...  In our privacy-preserving framework, we adopt (a, k)-anonymity as privacy-preserving model for D2D big data and use the distributed MapReduce to classify and group data for massive datasets.  ...  [22] proposed a cloud-oriented scalable big data privacy protection framework that can perform large-scale dataset anonymization and process anonymity datasets.  ... 
doi:10.1155/2019/2076542 fatcat:sdprvv524rephafzlqjxn4kfi4

Privacy-enhancing ETL-processes for biomedical data

Fabian Prasser, Helmut Spengler, Raffael Bild, Johanna Eicher, Klaus A. Kuhn
2019 International Journal of Medical Informatics  
Methods: Our main design goals were (1) to base the anonymization process on expert-level risk assessment methodologies, (2) to use transformation methods which preserve both the truthfulness of data and  ...  Results: We designed a novel and efficient anonymization process and implemented a plugin for the Pentaho Data Integration (PDI) platform, which enables integrating data anonymization and re-identification  ...  Data processing in PDI is stream-oriented with single rows of data constituting atomic and isolated units of a data stream. This means that data is passed through the ETL pipeline row by row.  ... 
doi:10.1016/j.ijmedinf.2019.03.006 fatcat:yhwxovtsp5b4tjmir56qsc3go4

Ensuring Privacy in Natural Language Processing: A PRISMA Oriented Literature Review [chapter]

Sima Alahyari, Matthias Hafner, Silvia Knittl
2022 Ambient Intelligence and Smart Environments  
Using Natural Language Processing (NLP) as a discipline of machine learning, organizations can better organize their data in order to better represent their internal knowledge.  ...  After following the identification process of significant sources, 22 valuable studies were selected.  ...  Anonymization on Textual Data: One of the benefits of Anonymization is that it is scalable toward privacy preservation, it means we can anonymize a sensitive attribute in a way that it reveals a part of  ... 
doi:10.3233/aise220038 fatcat:e5hapz4obbebznjr6jnthb6woq

A Survey of Privacy Preserving Data Publishing using Generalization and Suppression

Yang Xu, Tinghuai Ma, Meili Tang, Wei Tian
2014 Applied Mathematics & Information Sciences  
Recent work focuses on proposing different anonymity algorithms for varying data publishing scenarios to satisfy privacy requirements, and keep data utility at the same time.  ...  Numerous anonymity algorithms have been utilized for achieving k-anonymity.  ...  The authors are grateful to the anonymous referee for a careful checking of the details and for helpful comments that improved this paper.  ... 
doi:10.12785/amis/080321 fatcat:knofh4wnynd7zbpth6skdsgh2m

Data Anonymization through Collaborative Multi-view Microaggregation

Sarah Zouinina, Younès Bennani, Nicoleta Rogovschi, Abdelouahid Lyhyaoui
2020 Journal of Intelligent Systems  
The main challenge of data anonymization is to find a balance between data utility and the amount of disclosure risk.  ...  The four methods proposed were proven to be efficient using two data utility measures, the separability utility and the structural utility.  ...  The process of preserving data privacy is called data anonymization and was used for quite a while to statistical purposes.  ... 
doi:10.1515/jisys-2020-0026 fatcat:orj5ri4gjzbybpfaaomxx7mhvq

Privacy-Preserving Access Control in Electronic Health Record Linkage

Yang Lu, Richard O. Sinnott, Kain Verspoor, Udaya Parampalli
2018 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)  
We show how SLKA balances privacy and utility preservation through detecting risky combinations hidden in data releases.  ...  However traditional methods such as k-anonymity and its derivations are often overgeneralizing resulting in lower data accuracy.  ...  With fewer QI attributes being generalised, the weak k-anonymity model outperforms standard k-anonymity in terms of preserving the utility of linked data.  ... 
doi:10.1109/trustcom/bigdatase.2018.00151 dblp:conf/trustcom/LuSVP18 fatcat:irfxbuha4vd3vk32xcojk4jliu
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