An Efficient Microaggregation Method for Protecting Mixed Data

Chettri, Sarat Kumar; Borah, Bhogeswar

doi:10.1007/978-1-4614-6154-8_54

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PPDM methods seek to protect the anonymity of data owners when a data mining technique is applied on the protected dataset. Various perturbative and non-perturbative PPDM methods namely microaggregation, k-anonymity, data randomization, sampling and so on, are proposed in the literature [4,6,11,21].…”

Section: Introductionmentioning

confidence: 99%

On analysis of time-series data with preserved privacy

Chettri

Borah

2015

Innovations Syst Softw Eng

View full text Add to dashboard Cite

Time-series data analysis with privacy preservation is an open and challenging issue. To name a few are like analyzing company's confidential financial data, individual's health-related data, electricity consumption of individual's households and so on. Due to the complex nature of timeseries data, analyzing such data without any revelation of sensitive information to adversaries is a pervasive task. Here, we have addressed the issue of analyzing numerical timeseries of equal length with preserved privacy. Considering the Discrete Wavelet Transform as a suitable technique for transforming time-series in frequency-time representation, we have applied the concept in privacy-preserving analysis of such data. Experimental results show that our proposed method is superior to the existing methods in preserving the trade-off between data utility and privacy. The privacy models developed using the proposed method are also evaluated in terms of clustering and classification accuracies obtained from perturbed time-series data.

show abstract

Section: Introductionmentioning

confidence: 99%