Abstract
In this paper, we proposed a new privacy-preserving clustering framework. We proposed two different types of data transformation methods on clustering solution vectors and clustering ensemble consensus matrix in a unified way. The first one is encryption, which includes cryptography-based methods and hashing functions. The other one is the perturbation, which includes data swapping, spatial transformation, and randomized perturbation. The related transformed clustering pairs or the consensus matrix pairs are evaluated them using 19 popular pair-counting similarity measures. The original evaluation results and those of privacy-preserving methods are discussed according to different privacy-preserving methods and different pair-counting similarity measures. We found that 1) for clustering similarity, the information loss of clustering similarity in cryptography-based methods and hashing functions is generally large and not applicable to the clustering ensemble case, while the information loss of data swapping, spatial transformation, and randomized perturbation are small and applicable to a wide range. 2) Through experimental validation, we found that different privacy-preserving methods and different pair-counting similarity measures show certain adaptability, where five pair-counting similarity measures are stable for all types of privacy protection methods. 3) Cryptography and hashing functions have high security and too much complexity, while data swapping, spatial transformation, and randomized perturbation have low complexity but low security. The above work can be used as an original investigation in this field and as a reference for subsequent related future work.
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The work described in this paper was partially supported by grants from Guang-dong Natural Science Foundation of China No. 2018A030313922, the funding of Guangzhou education scientific research project No. 1201730714, and the National Natural Science Foundation of China under grant No. 61801133.
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Yang, P., Zhang, S., Yang, L. (2022). Privacy-Preserving Cluster Validity. In: Wang, G., Choo, KK.R., Ko, R.K.L., Xu, Y., Crispo, B. (eds) Ubiquitous Security. UbiSec 2021. Communications in Computer and Information Science, vol 1557. Springer, Singapore. https://doi.org/10.1007/978-981-19-0468-4_12
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