Privacy-preserving imputation of missing data

Jagannathan, Geetha; Wright, Rebecca N.

doi:10.1016/j.datak.2007.06.013

Cited by 29 publications

(16 citation statements)

References 19 publications

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“…Previous categories of PPDM allow disclose of data beyond the control of the data collection. Authors in [14] have addressed the problem of reconstructing missing values by building a data model where the parties are distributed and data is horizontally partitioned. A cryptographic protocol based on decision-tree classification is described by them.…”

Section: Cryptography Basedmentioning

confidence: 99%

Privacy Preserving Data Mining: Techniques, Classification and Implications - A Survey

Shah¹,

Gulati²

2016

IJCA

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Privacy has become crucial in knowledge based applications. Proper integration of individual privacy is essential for data mining operations. This privacy based data mining is important for sectors like Healthcare, Pharmaceuticals, Research, and Security Service Providers, to name a few. The main categorization of Privacy Preserving Data Mining (PPDM) techniques falls into Perturbation, Secure Sum Computations and Cryptographic based techniques. There exist tradeoffs between privacy preservation and information loss for generalized solutions. The authors of the paper present an extensive survey of PPDM techniques, their classification and give a preliminary implication of technique to be used under specific scenarios.

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Section: Cryptography Basedmentioning

confidence: 99%

Privacy Preserving Data Mining: Techniques, Classification and Implications - A Survey

Shah¹,

Gulati²

2016

IJCA

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“…Apart from the importance of the mining stage, which is significant in many applications, an increasing concern has been focused on the privacy threats that emerge from data mining. Consequently, numerous establishments regularly need to distribute partial data, which can be useful in enhancing the efficiency of organizations and aid their future plans [5], [11]- [13]. However, the human processing level is known for collecting large volumes of data, which increase exponentially [14].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Survey on Big Data Privacy Protection

et al. 2020

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In recent years, the ever-mounting problem of Internet phishing has been threatening the secure propagation of sensitive data over the web, thereby resulting in either outright decline of data distribution or inaccurate data distribution from several data providers. Therefore, user privacy has evolved into a critical issue in various data mining operations. User privacy has turned out to be a foremost criterion for allowing the transfer of confidential information. The intense surge in storing the personal data of customers (i.e., big data) has resulted in a new research area, which is referred to as privacy-preserving data mining (PPDM). A key issue of PPDM is how to manipulate data using a specific approach to enable the development of a good data mining model on modified data, thereby meeting a specified privacy need with minimum loss of information for the intended data analysis task. The current review study aims to utilize the tasks of data mining operations without risking the security of individuals' sensitive information, particularly at the record level. To this end, PPDM techniques are reviewed and classified using various approaches for data modification. Furthermore, a critical comparative analysis is performed for the advantages and drawbacks of PPDM techniques. This review study also elaborates on the existing challenges and unresolved issues in PPDM. INDEX TERMS Security, big data, privacy protection, privacy-preserving data mining.

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“…However, all the techniques mentioned previously might be hard to reflect the relationship among regression variables, since the imputed values were mere approximations of unknown values. Besides the statistical techniques, the machine learning was paying more and more attentions nowadays, as presented in [18,19].…”

Section: Introductionmentioning

confidence: 99%

An Improved Generalized-Trend-Diffusion-Based Data Imputation for Steel Industry

Liu

Wang

2013

Mathematical Problems in Engineering

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Integrality and validity of industrial data are the fundamental factors in the domain of data-driven modeling. Aiming at the data missing problem of gas flow in steel industry, an improved Generalized-Trend-Diffusion (iGTD) algorithm is proposed in this study, where in particular it considers the sort of problem with data properties of consecutively missing and small samples. And, the imputation accuracy can be greatly increased by the proposed Gaussian membership-based GTD which expands the useful knowledge of data samples. In addition, the imputation order is further discussed to enhance the sequential forecasting accuracy of gas flow. To verify the effectiveness of the proposed method, a series of experiments that consists of three categories of data features in the gas system is presented, and the results indicate that this method is comprehensively better for the imputation of the periodical-like data and the time-series-like data.

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Privacy-preserving imputation of missing data

Cited by 29 publications

References 19 publications

Privacy Preserving Data Mining: Techniques, Classification and Implications - A Survey

Privacy Preserving Data Mining: Techniques, Classification and Implications - A Survey

Comprehensive Survey on Big Data Privacy Protection

An Improved Generalized-Trend-Diffusion-Based Data Imputation for Steel Industry

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