Abstract
Perturbation-based techniques are studied extensively in context of privacy preserving data mining. In this technique, random noise is added to the original distribution for privacy sensitive data. Next, they are sent to the data miner who will reconstruct the original data distribution from the perturbed sets. Perturbation approaches project a trade-off between applicability of data versus privacy achieved. Also, different entities participating in collaborative data mining have different attitudes toward privacy based on their country’s customs and traditions. Contemporary-based perturbation techniques do not allow the participating entities to choose the privacy levels needed by them. This is main motivation our work. This paper presents a privacy level-based perturbation model opening new avenues for future work in this direction.
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Shah, A.K., Gulati, R. (2016). Privacy-Leveled Perturbation Model for Privacy Preserving Collaborative Data Mining. In: Satapathy, S., Joshi, A., Modi, N., Pathak, N. (eds) Proceedings of International Conference on ICT for Sustainable Development. Advances in Intelligent Systems and Computing, vol 409. Springer, Singapore. https://doi.org/10.1007/978-981-10-0135-2_22
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DOI: https://doi.org/10.1007/978-981-10-0135-2_22
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