SCIS 2022
2022 Symposium on
Cryptography and Information Security
Osaka, Japan & Online, Jan. 18 – 21, 2022
The Institute of Electronics,
Information and Communication Engineers

Copyright ©2022 The Institute of Electronics,
Information and Communication Engineers

Data Lineage Management with Unlearning Method for Machine
Learning Security and Privacy Issues
Haibo ZHANG ∗Toru NAKAMURA †Takamasa ISOHARA ‡Kouichi SAKURAI §Keywords: Data Lineage, Security, Privacy, Machine Learning, Machine Unlearning

Abstract

This paper provides an in-depth analysis of machinelearning models’ security and privacy concerns. Firstly,
we illustrate the privacy and security concerns based on
the review of related academic and industrial works.
Then, we compare the traditional retraining methodwith machine unlearning methods to solve security andprivacy protection issues. Furthermore, we also discuss
the future research direction and possibilities in thisfield.

Privacy protection has been a concern for researchers
for a long time. In today’s big data environment, users
interact with data on various web platforms, such assending and receiving emails, browsing news, etc., almost every day. For users, once they have provided
their information in an application, it is difficult to remove it from the root. When machine learning is widelyused today, most advanced features are obtained basedon understanding and training users’ data. As a result,users’ privacy has been spread in every corner of theapplication, makings it more accessible for attackers tosteal users’ private data.
Recently, an increasing number of laws have governed the useability of users’ privacy. For example,
Article 17 of the General Data Protection Regulation(GDPR), the right to be forgotten, requires machinelearning applications to remove a portion of data froma dataset and retrain it if the user makes such a request [1, 2]. This maintains the user’s right to use
their privacy from a privacy protection perspective [3].
From the security perspective, if an attacker compromises the machine learning model by injecting somepollution data into its dataset, it is also necessary toremove such data from the dataset and retrain it [4].
For example, an attacker can open a backdoor in a machine learning model by injecting malicious data into
the dataset used for training. The attacker can steal
all the private data in the model.
For solving the above problems, it is necessary to retrain the machine learning model. However, the existing retraining methods cause a large amount of computational power and time consumption. Therefore,
researchers propose machine unlearning as a more efficient research method [5].

Acknowledgement
This research is partially supported by the Japan
Science and Technology Agency (JST) Strategic International Collaborative Research Program (SICORP).

References[1] S. Schelter, “Towards efficient machine unlearning
via incremental view maintenance.”[2] L. Graves, V. Nagisetty, and V. Ganesh,“Amnesiac machine learning,” arXiv preprint
arXiv:2010.10981, 2020.
[3] M. Al-Rubaie and J. M. Chang, “Privacypreserving machine learning: Threats and solutions,” IEEE Security & Privacy, vol. 17, no. 2,
pp. 49–58, 2019.
[4] N. Baracaldo, B. Chen, H. Ludwig, and J. A. Safavi,“Mitigating poisoning attacks on machine learningmodels: A data provenance based approach,” inProceedings of the 10th ACM Workshop on Artificial Intelligence and Security, 2017, pp. 103–110.
[5] L. Bourtoule, V. Chandrasekaran, C. A. ChoquetteChoo, H. Jia, A. Travers, B. Zhang, D. Lie, andN. Papernot, “Machine unlearning,” in 2021 IEEE
Symposium on Security and Privacy (SP). IEEE,
2021, pp. 141–159.

∗Department of Information Science and Technology, Graduate School of Information Science and Electrical Engineering,Kyushu University (E-mail: haiboz0105@gmail.com)† KDDI Research Inc. (E-mail: tr-nakamura@kddi-research.jp)‡ KDDI Research Inc. (E-mail: ta-isohara@kddi-research.jp)§ Department of Information Science and Technology, Faculty ofInformation Science and Electrical Engineering, Kyushu University (E-mail: sakurai@inf.kyushu-u.ac.jp)1