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A General Framework Information Loss of Utility-Based Anonymization in Data Publishing

Waleed M.Ead, Et. al.
2021 Turkish Journal of Computer and Mathematics Education  
In this paper, we propose a general framework for the utility-based anonymization to minimize the information loss in data published with a trade-off grantee of achieving the required privacy level.  ...  To build anonymization, the data anonymizer must determine the following three issues: Firstly, which data to be preserved?  ...  is greater than the predefined thresholds defined by the data owner.  Anonymize sensitive data attributes  Return query results Sanitizing Knowledge Discovery Data mining methods are an effective use  ... 
doi:10.17762/turcomat.v12i5.2102 fatcat:wttiwhxxnng6zmlg5jn3dxbcdm

Maintening multi-level confidentiality on big data using Pk-anonymization methods and cryptographic techniques

Ahmed Mohammed, G. Babu
2018 Advances in Modelling and Analysis B  
Anonymization innovation is basic for accomplishing assurance on security when utilizing individual data. In the time of bigdata a lot of data has been aggregated by different data repositories.  ...  Security safeguarding and elevated function of Data is conceivable because of the guide diminishing structure and k-Anonymization expertise which can be effectively used with big data.In this manuscript  ...  The above figure illustrates system arrangement for assessment of big data with anonymization techniques [21, 24] .The framework covers a different put stock in levels for information to give security  ... 
doi:10.18280/ama_b.610103 fatcat:qwrpdw5dqrayno7ikjffcsmny4

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.  ...  Firstly, we provide a framework for the D2D big data sharing and analyze the threat model. Then, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce.  ...  [7] proposed a data collection framework with privacy protection capability in smart grids based on a key distribution framework that effectively protects the user's private data.  ... 
doi:10.1155/2019/2076542 fatcat:sdprvv524rephafzlqjxn4kfi4

HIDE: An Integrated System for Health Information DE-identification

James Gardner, Li Xiong
2008 2008 21st IEEE International Symposium on Computer-Based Medical Systems  
It deploys a conditional random fields based technique for extracting identifying attributes from unstructured data and k-anonymization based technique for de-identifying the data while preserving maximum  ...  While there is an increasing need to share medical information for public health research, such data sharing must preserve patient privacy without disclosing any identifiable information.  ...  Second, the conceptual framework of our system advances the data privacy field by integrating the anonymization process for both structured and unstructured data.  ... 
doi:10.1109/cbms.2008.129 dblp:conf/cbms/GardnerX08 fatcat:gyjvlr5flbfytfxe5slnovqwjq

Situating anonymization within a privacy risk model

Stuart S. Shapiro
2012 2012 IEEE International Systems Conference SysCon 2012  
Department of Homeland Security has sponsored development of both an integrated anonymization framework and an enhanced privacy risk model to support more effective privacy risk management.  ...  By interrelating an enhanced privacy risk model that goes beyond FIPPs and an integrated anonymization framework, the selection and implementation of anonymization as a privacy risk control can be more  ...  An integrated anonymization framework, therefore, can be structured as a data flow process in which specific manipulations are targeted at specific types of data with the goal of either moving the data  ... 
doi:10.1109/syscon.2012.6189494 fatcat:gujsnqgslndqzahx4fwk3mudy4

The Integrated Holistic Security and Privacy Framework Deployed in CrowdHEALTH Project

Stefanos Malliaros, Christos Xenakis, George Moldovan, John Mantas, Andriana Magdalinou, Lydia Montandon
2019 Acta Informatica Medica  
CrowdHEALTH integrates ABAC with OpenID Connect to build an effective and scalable base for end-users' authorization.  ...  , user authorization, access control, data anonymization, trust management and reputation modelling.  ...  For example, an administrator Data Anonymization In CrowdHEALTH, the procedure that is used to anonymize the data includes two stages.  ... 
doi:10.5455/aim.2019.27.333-340 pmid:32210501 pmcid:PMC7085323 fatcat:qb74yno6qzhobldwcpdhhwdfxm

Bigdata Anonymization Using One Dimensional and Multidimensional Map Reduce Framework on Cloud

Shalin Eliabeth S, Sarju S
2015 International Journal of Database Theory and Application  
Data anonymization refers to as hiding complex data for owners of data records.  ...  For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data.  ...  Data Specialization An original data set D is concretely specialized for anonymization [11] in a one-iteration in Map Reduce job.  ... 
doi:10.14257/ijdta.2015.8.6.23 fatcat:mayq7vjrmza2piacnnmxapnbnq

Adaptive Utility-based Anonymization Model: Performance Evaluation on Big Data Sets

Jisha Jose Panackal, Anitha S. Pillai
2015 Procedia Computer Science  
Data Anonymization is one of the globally accepted mechanisms for the protection of privacy of individuals in data publishing scenario.  ...  An intelligent approach based on association mining namely, Adaptive Utility-based Anonymization (AUA) has been proposed in order to deal with this issue.  ...  That is, if the anonymized data is to be used for a study based on a particular QI attribute, that user will get an anonymized version without affecting that attribute.  ... 
doi:10.1016/j.procs.2015.04.037 fatcat:g2ddazt37bgdhioshucc6fqsda

Structure Based Data De-Anonymization of Social Networks and Mobility Traces [chapter]

Shouling Ji, Weiqing Li, Mudhakar Srivatsa, Jing Selena He, Raheem Beyah
2014 Lecture Notes in Computer Science  
Then, to de-anonymize data without the knowledge of the overlap size between the anonymized data and the auxiliary data, we generalize DA to an Adaptive De-Anonymization (ADA) framework.  ...  First, we design an Unified Similarity (US) measurement, based on which we present a US based De-Anonymization (DA) framework which iteratively de-anonymizes data with an accuracy guarantee.  ...  The US and UK Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. Jing S.  ... 
doi:10.1007/978-3-319-13257-0_14 fatcat:6e4q7f2igbfgtlbkku5uzvf4ru

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  
The modem data is collected by using IoT, stored in distributed cloud storage, and issued for data mining or training artificial intelligence.  ...  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.  ...  For example, the attacker obtains the k-anonymized data. if the equivalence class of the k-anonymized data is all AIDS patients, then the attacker can easily make the judgment which one in the k-anonymized  ... 
doi:10.3233/apc210190 fatcat:7j5qgemtizbyroemvmodgje4ju

Privacy-Preserving Data Mining on Moving Object Trajectories

Gyozo Gidofalvi, Xuegang Huang, Torben Bach Pedersen
2007 2007 International Conference on Mobile Data Management  
The DB TECH REPORTS icon is made from two letters in an early version of the Rune alphabet, which was used by the Vikings, among others.  ...  For additional information, see the DB TECH REPORTS homepage: .  ...  Third, the paper presents an effective grid-based framework for data collection and mining over the anonymized trajectory data.  ... 
doi:10.1109/mdm.2007.18 dblp:conf/mdm/GidofalviHP07 fatcat:lcpik3nzongpbfj3n2jnr3sjgm

User-Side Personalization Considering Privacy Preserving in Cloud Systems

L. Sharifi, M. H. Beisafar
2013 2013 27th International Conference on Advanced Information Networking and Applications Workshops  
This method embraces personal data processing agent in the client side through personalization techniques and queries are sent to the hosts in an anonymous format.  ...  the hosts to access their personal data for personalization purposes.  ...  In storing method we mostly study if the framework proposes any encryption method, anonymization, or uses an engine for storing objective.  ... 
doi:10.1109/waina.2013.193 dblp:conf/aina/SharifiB13 fatcat:ldtkjy7is5bfzl2oazgfpndosm

An integrated framework for de-identifying unstructured medical data

James Gardner, Li Xiong
2009 Data & Knowledge Engineering  
We deploy a k-anonymization based technique for de-identifying the extracted data to preserve maximum data utility.  ...  This paper attempts to fill the above gaps and presents a framework and prototype system for de-identifying health information including both structured and unstructured data.  ...  We thank the guest editors and anonymous reviewers for their valuable comments that improved this paper.  ... 
doi:10.1016/j.datak.2009.07.006 fatcat:4eq3oldagrfmjnddkylluuhliy

A Protection Layer over MapReduce Framework for Big Data Privacy

Hidayath Ali Baig
2022 International Journal of Computer and Information Technology(2279-0764)  
The framework MapReduce, which is generally used for this purpose, has been accepted by most organizations for its exceptional characteristics.  ...  This article reviews some of the existing research articles on the MapReduce framework's privacy issues and proposes an additional layer of privacy protection over the adopted framework.  ...  MapReduce applications have core frameworks, such as data replication and data sorting, to effectively and efficiently make such a basic programming model function.  ... 
doi:10.24203/ijcit.v11i2.263 fatcat:6wcmqxvri5bjfcs7lk4qwefw6e

Scalable Privacy Preservation in Big Data a Survey

S. Vennila, J. Priyadarshini
2015 Procedia Computer Science  
Existing approaches employ local recording anonymization for privacy preserving where data are processed for analysis, mining and sharing.  ...  The MapReduce framework is most preferable for processing huge volume of unstructured data set in BigData.  ...  Data anonymization refers to hiding identity of sensitive data so that the privacy of an individual is effectively preserved even certain aggregate information can be still exposed to data users for diverse  ... 
doi:10.1016/j.procs.2015.04.033 fatcat:ptyt3zbxyjdqjpmc3vcnpqvqxe
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