An Efficient Algorithm for Privacy Preserving Data Mining Using Hybrid Transformation

Jalla, Hanumantha Rao; P.N, Girija

doi:10.5121/ijdkp.2014.4404

Cited by 3 publications

(1 citation statement)

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“…The results show higher privacy is attained on hybrid methods when compared to single data perturbation methods. A hybrid technique [7,8] based on Walsh-Hadamard Transformation (WHT) and Rotation is proposed. The Euclidean distance preserving transformation using Walsh-Hadamard (Hn) given below to generate orthogonal matrix to preserve statistical properties of the original dataset.…”

Section: Introductionmentioning

confidence: 99%

Multiplicative Data Perturbation Using Random Rotation Method

Jahan¹

2023

Data Integrity and Data Governance

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Today’s applications rely on large volumes of personal data being collected and processed regularly. Many unauthorized users try to access this private data. Data perturbation methods are one among many Privacy Preserving Data Mining (PPDM) techniques. They play a key role in perturbing confidential data. The research work focuses on developing an efficient data perturbation method using multivariate dataset which can preserve privacy in a centralized environment and allow publishing data. To carry out the data perturbation on a multivariate dataset, a Multiplicative Data Perturbation (MDP) using Random Rotation method is proposed. The results revealed an efficient multiplicative data perturbation using multivariate datasets which is resilient to attacks or threats and preserves the privacy in centralized environment.

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Section: Introductionmentioning

confidence: 99%