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Subspace based noise addition for privacy preserved data mining on high dimensional continuous data

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Abstract

Clustering is an important data mining technique. Due to privacy concerns associated with data mining, privacy preservation algorithms have been developed in the last decade. Noise addition is a popular privacy preservation technique. However, in recent years a lot of applications have been dealing with high dimensional datasets. The present privacy-preserving algorithms perform noise addition in a univariate manner along each dimension. This approach does not work well with high dimensional continuous datasets because the distance measurements that contrast a point between similar and non-similar is less and data groups differently in different dimensions. Therefore information loss and data loss are very high, clusters identified are reduced drastically and sometimes even wrong clusters are identified. Data mining performed on such data is ineffective and sometimes invalid. This paper proposes a novel technique called subspace based noise addition (SBNA) that adds noise in subspaces. Dense and Non-dense subspaces are identified and noise addition is then performed separately in dense and non-dense subspaces. Noise is added such that points lying in dense and non-dense subspaces continue to lie in their respective subspaces even after privacy preservation. This approach reduces data loss, information loss and maximizes the identification of clusters. This ensures effective data mining. Experiments are performed on benchmark high dimensional continuous datasets from UCI Machine Learning Repository and the results are compared with the related works like SNA, NALT, and NANLT. SBNA provides an improvement of up to 80% in data utility, 90% in cluster identification and information measures.

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Authors and Affiliations

  1. Department of CSE, VFSTR (Deemed to be University), Guntur, India

    Shashidhar Virupaksha & Venkatesulu Dondeti

  2. Department of CSE, Presidency University, Bengaluru, India

    Shashidhar Virupaksha

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  1. Shashidhar Virupaksha
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  2. Venkatesulu Dondeti
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Correspondence to Shashidhar Virupaksha.

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Virupaksha, S., Dondeti, V. Subspace based noise addition for privacy preserved data mining on high dimensional continuous data. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-01881-8

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