Gradient Boosted Trees for Privacy-Preserving Matching of Encrypted Images
Jakub Pokrywka · 2022 IEEE International Conference on Big Data (Big Data) · 2022
A huge amount of data of various types is stored on servers and transferred over the Internet. This may pose a threat to the privacy of individuals. Encryption algorithms are developed to protect privacy. However, not all encryption mechanisms are effective. The easiest way of proving such ineffectiveness is by presenting a method of breaking the encryption scheme. In this paper, one method for matching original and encrypted images is proposed. The method was developed under the Privacy-preserving Matching of Encrypted Images shared task in the IEEE BigData 2022 Cup, and achieved second place with an accuracy of 0.6915, trailing the winning solution by only 0.0031. The proposed solution is based on Gradient Boosted Trees as implemented in CatBoost, and feature extraction of pixel value aggregates regardless of their positions. This is done using Arnold’s cat map obfuscation scheme in the encryption algorithm. Arnold’s cat map shuffles pixel positions, but leaves the image histogram unchanged. The method described in this paper almost solves two of the subtasks in the competition, reaching 0.98 accuracy for both of them; however, it does not propose a solution to the third subtask.