Protecting Personal Information using Homomorphic Encryption for Person Re-identification

[Abstract]

We investigate how to protect features corresponding to personal information using homomorphic encryption when matching people in several camera views. Homomorphic encryption can compute a distance between features without decryption. Thus, our method is able to use a computing server on a public network while protecting personal information. To apply homomorphic encryption, our method uses linear quantization to represent each element of the feature as integers. Experimental results show that there is no significant difference in the accuracy of person re-identification with or without homomorphic encryption and linear quantization.

[Publications]

• Shogo Fukuda, Masashi Nishiyama, Yoshio Iwai,
  Reduction in Communication via Image Selection for Homomorphic Encryption-based Privacy-Protected Person Re-identification,
  Proceedings of 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), Vol. 5: VISAPP, pp. 36 - 47, February 2021. [Video]
• Kazunari Morita, Hiroki Yoshimura, Masashi Nishiyama, Yoshio Iwai,
  Protecting Personal Information using Homomorphic Encryption for Person Re-identification,
  Proceedings of IEEE 7th Global Conference on Consumer Electronics (GCCE), pp. 135 - 136, October 2018. [Student Paper Award]
• 福田 尚悟, 森田 一成, 西山 正志, 岩井 儀雄,
  準同型暗号を用いた人物対応付けのための画像選択による通信量の削減,
  電子情報通信学会論文誌 D, Vol.J103-D, No.10, pp. 721 - 732, October 2020.