A comprehensive review on privacy preserving data mining
By Yousra Abdul Alsahib S. Aldeen, Mazleena Salleh, Mohammad Abdur Razzaque. 2015
130 citations recorded by the source corpus.
Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. Ever-escalating internet phishing posed severe threat on widespread propagation of sensitive information over the web. Conversely, the dubious feelings and contentions mediated unwillingness of various information providers towards the reliability protection of data from disclosure often results utter rejection in data sharing or incorrect information sharing. This article provides a panoramic overview on new perspective and systematic interpretation of a list published literatures via their meticulous organization in subcategories. The fundamental notions of the existing privacy preserving data mining methods, their merits, and shortcomings are presented. The current privacy preserving data mining techniques are classified based on distortion, association rule, hide association rule, taxonomy, clustering, associative classification, outsourced data mining, distributed, and k-anonymity, where their notable advantages and disadvantages are emphasized. This careful scrutiny reveals the past development, present research challenges, future trends, the gaps and weaknesses. Further significant enhancements for more robust privacy protection and preservation are affirmed to be mandatory.
Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Dino Pedreschi
- Fosca Giannotti
- Riccardo Guidotti
- Anna Monreale
- Franco Turini
- Salvatore Ruggieri
- Indrė Žliobaitė
- Sara Hajian
In Information Systems
- Franco Turini
- Fosca Giannotti
- Anna Monreale
- Dino Pedreschi
- Riccardo Guidotti
- Sara Hajian
- Salvatore Ruggieri
- Andrea Romei
In Sociology and Political Science
- Fosca Giannotti
- Dino Pedreschi
- Riccardo Guidotti
- Franco Turini
- Anna Monreale
- Indrė Žliobaitė
- Xintao Wu
- Lu Zhang
Citations in the corpus
- A Survey of Methods for Explaining Black Box Models (2018, background): “The first principle requires that the model guarantees the protection of groups against (direct or indirect) discrimination [100]; while the second one requires that the model does not reveal sensitive information about people [4].”