(k, e)-anonymous for ordinal data

(k, e)-anonymous for ordinal data

© 2015 IEEE. Currently, the data can be gathered, analyzed, and utilized easier than ever with the aiding of Big Data technologies such as mobile devices, elastic computing platform, or convenient software tools. Thus, privacy of such data could become a bigger issue as well. In this paper, we propo...

Saved in:

書目詳細資料
Main Authors:	Surapon Riyana, Nattapon Harnsamut, Torsak Soontornphand, Juggapong Natwichai
格式:	Conference Proceeding
出版:	2018
主題:	Computer Science
在線閱讀:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84964928536&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/54311
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Chiang Mai University

相似書籍

(k, e)-anonymous for ordinal data
由: Surapon Riyana, et al.
出版: (2018)

Privacy preservation for trajectory data publishing by look-up table generalization
由: Nattapon Harnsamut, et al.
出版: (2018)

Privacy preservation for re-publication data by using probabilistic graph
由: Pachara Tinamas, et al.
出版: (2020)

Privacy preservation based on full-domain generalization for incremental data publishing
由: Torsak Soontornphand, et al.
出版: (2018)

An efficient algorithm for incremental privacy breach on (k, e)-anonymous model
由: Bowonsak Srisungsittisunti, et al.
出版: (2018)

Achieving k-anonymity for associative classification in incremental-data scenarios
由: Bowonsak Seisungsittisunti, et al.
出版: (2018)

Data anonymization: a novel optimal k-anonymity algorithm for identical generalization hierarchy data in IoT
由: Waranya Mahanan, et al.
出版: (2020)

Joint Attack: A New Privacy Attack for Incremental Data Publishing
由: Torsak Soontornphand, et al.
出版: (2018)

An efficient algorithm for incremental privacy breach on (k, e)-anonymous model
由: Bowonsak Srisungsittisunti, et al.
出版: (2018)

A novel heuristic algorithm for privacy preserving of associative classification
由: Nattapon Harnsamut, et al.
出版: (2018)

An incremental privacy-preservation algorithm for the (k, e)-Anonymous model
由: Srisungsittisunti,B., et al.
出版: (2015)

Privacy preservation for recommendation databases
由: Surapon Riyana, et al.
出版: (2018)

Data quality in privacy preservation for associative classification
由: Nattapon Harnsamut, et al.
出版: (2018)

Incremental processing and indexing for (k, e)-anonymisation
由: Juggapong Natwichai, et al.
出版: (2018)

Achieving k-anonymity for associative classification in incremental-data scenarios
由: Seisungsittisunti B., et al.
出版: (2014)

Privacy preservation based on full-domain generalization for incremental data publishing
由: Soontornphand T., et al.
出版: (2017)

Utility-driven k-anonymization of public transport user data
由: Bhati, Bhawani Shanker, et al.
出版: (2021)

Utility-Oriented K-Anonymization on Social Networks
由: WANG, Yazhe, et al.
出版: (2011)

An efficient indexing for top-k query answering in location-based recommendation system
由: Sudarat Yawutthi, et al.
出版: (2018)

kbe-Anonymity: Test Data Anonymization for Evolving Programs
由: Lucia, -, et al.
出版: (2012)

Ordinal regression based on data relationship
由: Liu, Yanzhu
出版: (2019)

Privacy preserving of associative classification and heuristic approach
由: Nattapon Hamsamut, et al.
出版: (2018)

k-Anonymity in the Presence of External Databases
由: SACHARIDIS, Dimitris, et al.
出版: (2010)

Issues in Privacy Preservation for RePublishable Data
由: Pachara Tinamas, et al.
出版: (2020)

An efficient approach for data-duplication detection based on RDBMS
由: Kiettisak Chanhom, et al.
出版: (2018)

Incremental processing and indexing for (k, e)-anonymisation
由: Juggapong Natwichai, et al.
出版: (2018)

Efficient data update for location-based recommendation systems
由: Narin Jantaraprapa, et al.
出版: (2018)

K-anonymity diagnosis centre
由: Mirakabad, M.R.Z., et al.
出版: (2013)

Transductive ordinal regression
由: Seah, Chun-Wei, et al.
出版: (2013)

Data reduction approach for sensitive associative classification rule hiding
由: Juggapong Natwichai, et al.
出版: (2018)

Centrality-burst detection in social networks: An efficient approach for data stream
由: Waranya Mahanan, et al.
出版: (2018)

Joint Attack: A New Privacy Attack for Incremental Data Publishing
由: Soontornphand T., et al.
出版: (2017)

kbe-anonymity: Test data anonymization for evolving programs
由: LUCIA, Lucia, et al.
出版: (2012)

An approximation algorithm for privacy preservation of associative classification
由: Juggapong Natwichai
出版: (2018)

A heuristic data reduction approach for associative classification rule hiding
由: Juggapong Natwichai, et al.
出版: (2018)

Dahs: a distributed data-as-a-service framework for data analytics in healthcare
由: Pruet Boonma, et al.
出版: (2020)

Anonymous reputation system
由: Chua, Wei Kiat
出版: (2017)

Query transformation for exact cardinality computing
由: Prariwat Kwanglat, et al.
出版: (2018)

Reliable cluster on uncertain multigraph
由: Pruet Boonma, et al.
出版: (2018)

kb-anonymity: A model for anonymized behavior-preserving test and debugging data
由: BUDI, Aditya, et al.
出版: (2011)