Introduction to Privacy-Preserving Data Publishing (2025)

Privacy Preservation for Knowledge Discovery: A Survey

IOSR Journal of Computer Engineering, 2013

Today's globally networked society places great demand on the dissemination and sharing of information. Privacy Preservation is an important issue in the release of data for mining purposes. How to efficiently protect individual privacy in data publishing is especially critical. With releasing of microdata such as social security number disease by some organization should contain privacy in data publishing. Data holders can remove explicit identifiers to gain privacy but other attributes which are in published data can lead to reveal privacy to adversary. So several methods such as K-anonymity, L-diversity, T-closeness, (n,t) closeness, (α,k)-anonymization, p-sensitive k-anonymity and others method come into existence to maintain privacy in data publishing.

A General Survey of Privacy-Preserving Data Mining Models and Algorithms

In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. A number of algorithmic techniques have been designed for privacy-preserving data mining. In this paper, we provide a review of the state-of-the-art methods for privacy. We discuss methods for randomization, k-anonymization, and distributed privacy-preserving data mining. We also discuss cases in which the output of data mining applications needs to be sanitized for privacy-preservation purposes. We discuss the computational and theoretical limits associated with privacy-preservation over high dimensional data sets.

An overview of privacy preserving data mining

Crossroads, 2009

As it becomes evident, there exists an extended set of application scenarios in which information or knowledge derived from the data must be shared with other (possibly untrusted) entities. The sharing of data and/or knowledge may come at a cost to privacy, primarily due to two reasons:

Data Privacy in Data Engineering, the Privacy Preserving Models and Techniques in Data Mining and Data Publishing: Contemporary Affirmation of the Recent Literature

International Journal of Computer Applications, 2012

Privacy preserving for data engineering methods like mining and publishing etc., with the advancement of the rapid development of technologies like Internet and distributed computing has turned out to be one of the most important research areas of interest and has also triggered a serious issue of concern in accordance with the personal data usage in the recent times. Effective analysis result and gathering accurate data is desired by data users in specific, in contrast to the data owners who are concerned as their data contains personal information like the ones in government departments, Health insurance organizations and hospitals and data mining and warehouse utilities, where privacy is an issue to be taken rather seriously. Hence various proposals have been designated in data engineering methods publishing and mining for the purpose of preserving privacy. This paper briefs about the classification of the various privacy preserving approaches in data engineering, scans the current state of the art in lieu of preserving privacy of data, as also reviewing of the pros and cons of these specified approaches.

Attacks on Anonymization-Based Privacy-Preserving: A Survey for Data Mining and Data Publishing

2013

Data mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the party running the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model [1] which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.

IJERT-A Survey on Privacy Preserving Data Mining Techniques

International Journal of Engineering Research and Technology (IJERT), 2019

https://www.ijert.org/a-survey-on-privacy-preserving-data-mining-techniques https://www.ijert.org/research/a-survey-on-privacy-preserving-data-mining-techniques-IJERTCONV7IS05006.pdf The emerging privacy concern has become a major obstacle in storing and sharing of data. The proliferation of data can be useful, but it must be performed in a way that preserves user's privacy. This is not straightforward, because the proliferated data need to be protected against several privacy threats. Various algorithms have been designed for privacy-preserving data mining, that can be classified into three categories i.e., privacy by policy, privacy by statistics, and privacy by cryptography. We review algorithms like; Randomization, k-anonymization, and distributed privacy-preserving data mining etc., derive insights on their operation, and compare their advantages and disadvantages. We also provide a study of the computational and hypothetical boundaries involved with privacy-preservation over high dimensional data sets.

IJERT-A survey on maintaining privacy in data mining

International Journal of Engineering Research and Technology (IJERT), 2012

https://www.ijert.org/a-survey-on-maintaining-privacy-in-data-mining https://www.ijert.org/research/a-survey-on-maintaining-privacy-in-data-mining-IJERTV1IS1004.pdf Data Mining is the process of discovering new patterns from large datasets. The goal is to extract knowledge from dataset in human understandable structure. Now a day we all are using internet lot, data processing technologies, privacy of data is a major issue in data mining .So Privacy Preserving Data Mining has become very popular and in high demand. A number of methods and techniques have been developed for privacy preserving data mining. This paper provides a wide survey of different privacy preserving data mining algorithms. I have discussed more about one of algorithm Randomization and also discussed merits and demerits of the same.

Preface to the Fourth IEEE Workshop on Privacy Aspects of Data Mining

2013 IEEE 13th International Conference on Data Mining Workshops, 2013

State-of-the-art in privacy preserving data mining

ACM Sigmod …, 2004

We provide here an overview of the new and rapidly emerging research area of privacy preserving data mining. We also propose a classification hierarchy that sets the basis for analyzing the work which has been performed in this context. A detailed review of the work accomplished in this area is also given, along with the coordinates of each work to the classification hierarchy. A brief evaluation is performed, and some initial conclusions are made.

A survey on privacy preserving data mining

… International Workshop on Database …, 2009

Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes .So people have become increasingly unwilling to share their data, frequently resulting in individuals either refusing to share their data or providing incorrect data. In recent years, privacy preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. We discuss method for randomization, kanonymization, and distributed privacy preserving data mining. Knowledge is supremacy and the more knowledgeable we are about information break-in, we are less prone to fall prey to the evil hacker sharks of information technology. In this paper, we provide a review of the state-of-the-art methods for privacy and analyze the representative technique for privacy preserving data mining and points out their merits and demerits. Finally the present problems and directions for future research are discussed.