scholarly journals Bit Transformation Perturbative Masking Technique for Protecting Sensitive Information In Privacy Preserving Data Mining

2010 ◽  
Vol 2 (4) ◽  
pp. 107-114 ◽  
Author(s):  
S Vijayarani ◽  
A Tamilarasi
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Privacy Preserving Data Mining

Privacy Preserving Data Mining, Concepts, Techniques, and Evaluation Methodologies

2008 ◽  
pp. 2379-2401 ◽  
Author(s):  
Igor Nai Fovino
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Privacy Preserving ◽  
Research Field ◽  
Sensitive Information ◽  
Discovery Process ◽  
Privacy Preserving Data Mining ◽  
Mining Technology ◽  
Evaluation Methodologies ◽  
New Research

Intense work in the area of data mining technology and in its applications to several domains has resulted in the development of a large variety of techniques and tools able to automatically and intelligently transform large amounts of data in knowledge relevant to users. However, as with other kinds of useful technologies, the knowledge discovery process can be misused. It can be used, for example, by malicious subjects in order to reconstruct sensitive information for which they do not have an explicit access authorization. This type of “attack” cannot easily be detected, because, usually, the data used to guess the protected information, is freely accessible. For this reason, many research efforts have been recently devoted to addressing the problem of privacy preserving in data mining. The mission of this chapter is therefore to introduce the reader in this new research field and to provide the proper instruments (in term of concepts, techniques and example) in order to allow a critical comprehension of the advantages, the limitations and the open issues of the Privacy Preserving Data Mining Techniques.

DCT Image Steganography Analysis for Privacy Preserving Data Mining

2016 ◽  
Vol 7 (3) ◽  
pp. 1-9 ◽  
Author(s):  
Sahar A. El-Rahman Ismail ◽  
Dalal Al Makhdhub ◽  
Amal A. Al Qahtani ◽  
Ghadah A. Al Shabanat ◽  
Nouf M. Omair ◽  
...  
Keyword(s):  
Data Mining ◽  
Information Hiding ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Signal To Noise ◽  
Privacy Preserving Data Mining ◽  
Frequency Domains ◽  
Privacy And Confidentiality

We live in an information era where sensitive information extracted from data mining systems is vulnerable to exploitation. Privacy preserving data mining aims to prevent the discovery of sensitive information. Information hiding systems provide excellent privacy and confidentiality, where securing confidential communications in public channels can be achieved using steganography. A cover media are exploited using steganography techniques where they hide the payload's existence within appropriate multimedia carriers. This paper aims to study steganography techniques in spatial and frequency domains, and then analyzes the performance of Discrete Cosine Transform (DCT) based steganography using the low frequency and the middle frequency to compare their performance using Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The experimental results show that middle frequency has the larger message capacity and best performance.

Privacy Preserving Data Mining, Concepts, Techniques, and Evaluation Methodologies

2008 ◽  
pp. 277-301
Author(s):  
Igor Nai Fovino
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Privacy Preserving ◽  
Research Field ◽  
Sensitive Information ◽  
Discovery Process ◽  
Privacy Preserving Data Mining ◽  
Mining Technology ◽  
Evaluation Methodologies ◽  
New Research

Intense work in the area of data mining technology and in its applications to several domains has resulted in the development of a large variety of techniques and tools able to automatically and intelligently transform large amounts of data in knowledge relevant to users. However, as with other kinds of useful technologies, the knowledge discovery process can be misused. It can be used, for example, by malicious subjects in order to reconstruct sensitive information for which they do not have an explicit access authorization. This type of “attack” cannot easily be detected, because, usually, the data used to guess the protected information, is freely accessible. For this reason, many research efforts have been recently devoted to addressing the problem of privacy preserving in data mining. The mission of this chapter is therefore to introduce the reader in this new research field and to provide the proper instruments (in term of concepts, techniques and example) in order to allow a critical comprehension of the advantages, the limitations and the open issues of the Privacy Preserving Data Mining Techniques.

scholarly journals Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chun-Wei Lin ◽  
Tzung-Pei Hong ◽  
Hung-Chuan Hsu
Keyword(s):  
Data Mining ◽  
Side Effects ◽  
Execution Time ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Privacy Preserving Data Mining ◽  
Confidential Data

Data mining is traditionally adopted to retrieve and analyze knowledge from large amounts of data. Private or confidential data may be sanitized or suppressed before it is shared or published in public. Privacy preserving data mining (PPDM) has thus become an important issue in recent years. The most general way of PPDM is to sanitize the database to hide the sensitive information. In this paper, a novel hiding-missing-artificial utility (HMAU) algorithm is proposed to hide sensitive itemsets through transaction deletion. The transaction with the maximal ratio of sensitive to nonsensitive one is thus selected to be entirely deleted. Three side effects of hiding failures, missing itemsets, and artificial itemsets are considered to evaluate whether the transactions are required to be deleted for hiding sensitive itemsets. Three weights are also assigned as the importance to three factors, which can be set according to the requirement of users. Experiments are then conducted to show the performance of the proposed algorithm in execution time, number of deleted transactions, and number of side effects.

Collusion-Free Privacy Preserving Data Mining

2010 ◽  
Vol 6 (4) ◽  
pp. 30-45 ◽  
Author(s):  
M. Rajalakshmi ◽  
T. Purusothaman ◽  
S. Pratheeba
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Privacy Preserving ◽  
Frequent Itemsets ◽  
Data Sources ◽  
Sensitive Information ◽  
Distributed Data ◽  
Distributed Environment ◽  
Rule Mining ◽  
Privacy Preserving Data Mining

Distributed association rule mining is an integral part of data mining that extracts useful information hidden in distributed data sources. As local frequent itemsets are globalized from data sources, sensitive information about individual data sources needs high protection. Different privacy preserving data mining approaches for distributed environment have been proposed but in the existing approaches, collusion among the participating sites reveal sensitive information about the other sites. In this paper, the authors propose a collusion-free algorithm for mining global frequent itemsets in a distributed environment with minimal communication among sites. This algorithm uses the techniques of splitting and sanitizing the itemsets and communicates to random sites in two different phases, thus making it difficult for the colluders to retrieve sensitive information. Results show that the consequence of collusion is reduced to a greater extent without affecting mining performance and confirms optimal communication among sites.

Collusion-Free Privacy Preserving Data Mining

2012 ◽  
pp. 269-284
Author(s):  
T. Purusothaman ◽  
M. Rajalakshmi ◽  
S. Pratheeba
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Frequent Itemsets ◽  
Data Sources ◽  
Sensitive Information ◽  
Distributed Data ◽  
Distributed Environment ◽  
Rule Mining ◽  
Privacy Preserving Data Mining ◽  
Distributed Association

Distributed association rule mining is an integral part of data mining that extracts useful information hidden in distributed data sources. As local frequent itemsets are globalized from data sources, sensitive information about individual data sources needs high protection. Different privacy preserving data mining approaches for distributed environment have been proposed but in the existing approaches, collusion among the participating sites reveal sensitive information about the other sites. In this paper, the authors propose a collusion-free algorithm for mining global frequent itemsets in a distributed environment with minimal communication among sites. This algorithm uses the techniques of splitting and sanitizing the itemsets and communicates to random sites in two different phases, thus making it difficult for the colluders to retrieve sensitive information. Results show that the consequence of collusion is reduced to a greater extent without affecting mining performance and confirms optimal communication among sites.

Association Rule Hiding in Privacy Preserving Data Mining

2021 ◽  
pp. 963-986
Author(s):  
S. Vijayarani Mohan ◽  
Tamilarasi Angamuthu
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Association Rules ◽  
Association Rule ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Hidden Information ◽  
Privacy Preserving Data Mining ◽  
Marketing Information ◽  
Mining Association Rule

This article describes how privacy preserving data mining has become one of the most important and interesting research directions in data mining. With the help of data mining techniques, people can extract hidden information and discover patterns and relationships between the data items. In most of the situations, the extracted knowledge contains sensitive information about individuals and organizations. Moreover, this sensitive information can be misused for various purposes which violate the individual's privacy. Association rules frequently predetermine significant target marketing information about a business. Significant association rules provide knowledge to the data miner as they effectively summarize the data, while uncovering any hidden relations among items that hold in the data. Association rule hiding techniques are used for protecting the knowledge extracted by the sensitive association rules during the process of association rule mining. Association rule hiding refers to the process of modifying the original database in such a way that certain sensitive association rules disappear without seriously affecting the data and the non-sensitive rules. In this article, two new hiding techniques are proposed namely hiding technique based on genetic algorithm (HGA) and dummy items creation (DIC) technique. Hiding technique based on genetic algorithm is used for hiding sensitive association rules and the dummy items creation technique hides the sensitive rules as well as it creates dummy items for the modified sensitive items. Experimental results show the performance of the proposed techniques.

Privacy Preserving Data Mining

2010 ◽  
pp. 125-141
Author(s):  
Aris Gkoulalas-Divanis ◽  
Vassilios S. Verykios
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Future Research ◽  
Sensitive Information ◽  
Privacy Preserving Data Mining ◽  
Research Directions ◽  
Mobility Data ◽  
Future Research Directions ◽  
The Government ◽  
Existing Data

Since its inception in 2000, privacy preserving data mining has gained increasing popularity in the data mining research community. This line of research can be primarily attributed to the growing concern of individuals, organizations and the government regarding the violation of privacy in the mining of their data by the existing data mining technology. As a result, a whole new body of research was introduced to allow for the mining of data, while at the same time prohibiting the leakage of any private and sensitive information. In this chapter, the authors introduce the readers to the field of privacy preserving data mining; they discuss the reasons that led to its inception, the most prominent research directions, as well as some important methodologies per direction. Following that, the authors focus their attention on very recently investigated methodologies for the offering of privacy during the mining of user mobility data. In the end of the chapter, they provide a roadmap along with potential future research directions both with respect to the field of privacy-aware mobility data mining and to privacy preserving data mining at large.

scholarly journals Classification of Privacy Preserving Data Mining Algorithms: A Review

2020 ◽  
Vol 20 (2) ◽  
pp. 36
Author(s):  
Dedi Gunawan
Keyword(s):  
Data Mining ◽  
Extraction Process ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Time Data ◽  
Privacy Preserving Data Mining ◽  
Data Mining Techniques ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

Nowadays, data from various sources are gathered and stored in databases. The collection of the data does not give a significant impact unless the database owner conducts certain data analysis such as using data mining techniques to the databases. Presently, the development of data mining techniques and algorithms provides significant benefits for the information extraction process in terms of the quality, accuracy, and precision results. Realizing the fact that performing data mining tasks using some available data mining algorithms may disclose sensitive information of data subject in the databases, an action to protect privacy should be taken into account by the data owner. Therefore, privacy preserving data mining (PPDM) is becoming an emerging field of study in the data mining research group. The main purpose of PPDM is to investigate the side effects of data mining methods that originate from the penetration into the privacy of individuals and organizations. In addition, it guarantees that the data miners cannot reveal any personal sensitive information contained in a database, while at the same time data utility of a sanitized database does not significantly differ from that of the original one. In this paper, we present a wide view of current PPDM techniques by classifying them based on their taxonomy techniques to differentiate the characteristics of each approach. The review of the PPDM methods is described comprehensively to provide a profound understanding of the methods along with advantages, challenges, and future development for researchers and practitioners.

Privacy-Preserving Data Mining

2009 ◽  
pp. 527-536 ◽  
Author(s):  
Alexandre Evfimievski ◽  
Tyrone Grandison
Keyword(s):  
Data Mining ◽  
Privacy Preservation ◽  
Statistical Processing ◽  
Privacy Preserving ◽  
Third Party ◽  
Data Publishing ◽  
Sensitive Information ◽  
Public Servants ◽  
Data Set ◽  
Privacy Preserving Data Mining

Privacy-preserving data mining (PPDM) refers to the area of data mining that seeks to safeguard sensitive information from unsolicited or unsanctioned disclosure. Most traditional data mining techniques analyze and model the data set statistically, in aggregated form, while privacy preservation is primarily concerned with protecting against disclosure of individual data records. This domain separation points to the technical feasibility of PPDM. Historically, issues related to PPDM were first studied by the national statistical agencies interested in collecting private social and economical data, such as census and tax records, and making it available for analysis by public servants, companies, and researchers. Building accurate socioeconomical models is vital for business planning and public policy. Yet, there is no way of knowing in advance what models may be needed, nor is it feasible for the statistical agency to perform all data processing for everyone, playing the role of a trusted third party. Instead, the agency provides the data in a sanitized form that allows statistical processing and protects the privacy of individual records, solving a problem known as privacypreserving data publishing. For a survey of work in statistical databases, see Adam and Wortmann (1989) and Willenborg and de Waal (2001).

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