Machine Un-learning: An Overview of Techniques, Applications, and Future Directions

Goldsteen A, Ezov G, Shmelkin R, Moffie M, Farkash A. Data minimization for gdpr compliance in machine learning models. AI and Ethics. 2021;1–15.

Mourby M, Cathaoir KO´, Collin CB. Transparency of machine-learning in healthcare: The gdpr & european health law. Comput Law Secur Rev. 2021;43:105611.

General data protection regulation (gdpr) – official legal text. https://gdpr-info.eu/. Accessed 23 Jun 2023.

Everything you need to know about the right to be forgotten - gdpr.eu. https://gdpr.eu/right-to-be-forgotten/. Accessed 23 Jun 2023.

Is the ‘right to be forgotten’ a fundamental right? https://timesofindia.indiatimes.com/readersblog/myblogpost/is-the-right-to-be-forgotten-a-fundamental-right-52529/. Accessed 23 Jun 2023.

Voigt P, Von A, dem Bussche, The EU general data protection regulation (gdpr), A Practical Guide, 1st Ed., Cham: Springer Inter- national Publishing. 2017;10(3152676):10–5555.

Strobel M, Aspects of transparency in machine learning, in Proceed- ings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. 2019;2449–2451.

Lu¨ L, Medo M, Yeung CH, Zhang Y-C, Zhang Z-K, Zhou T. Recommender systems. Phys Rep. 2012;519(1):1–49.

Resnick P, Varian HR. Recommender systems. Communica- tions of the ACM. 1997;40(3):56–8.

Article  Google Scholar 

Kaelbling LP, Littman ML, Moore AW. Reinforcement learning: a survey. J Art Intell Res. 1996;4:237–85.

Ullman RH. Redefining security. Int Secur. 1983;8(1):129–53.

Article  MathSciNet  Google Scholar 

Westin AF. Privacy and freedom. Washington and Lee Law Rev. 1968;25(1):166.

Jordan PW. An introduction to usability. Crc Press. 1998.

Facebook sued over Cambridge analytica data scandal - bbc news. https://www.bbc.com/news/technology-54722362. Accessed 23 Jun 2023.

Google faces mass legal action in uk over data snooping - bbc news. https://www.bbc.com/news/technology-42166089. Accessed 23 Jun 2023.

California consumer privacy act (CCPA) — state of California - Department of Justice - Office of the attorney general, https://oag.ca.gov/privacy/ccpa. Accessed 8 Jul 2023.

World investment report 2020 — unctad. https://unctad.org/publication/world-investment-report-2020. Accessed 23 Jun 2023.

Mutual legal assistance treaties — department of legal affairs, mol &j, goi. https://legalaffairs.gov.in/documents/mlat. Accessed 23 Jun 2023.

Data protection committee report.pdf. https://www.meity.gov.in/writereaddata/files/DataProtectionommitteeReport.pdf. Accessed 23 Jun 2023.

4173ls(pre).p65. http://164.100.47.4/BillsTexts/LSBillTexts/Asintroduced/3732019LSEng.pdf. Accessed 23 Jun 2023.

Explained: Indian government makes user data collection mandatory for vpns — business insider India. https://www.businessinsider.in/tech/news/. Continual lifelong learning with neural networks: A review. Neural Networks. 2019:113; 54–71.

Mercuri S, Khraishi R, Okhrati R, Batra D, Hamill C, Ghasem- pour T, Nowlan A. An introduction to machine unlearning. arXiv preprint. http://arxiv.org/abs/2209.00939. 2022.

Ayyagari R. An exploratory analysis of data breaches from 2005–2011: trends and insights. J Inf Priv Secur. 2012;8(2):33–56.

Google Scholar 

Li Y, Liu Q. A comprehensive review study of cyber-attacks and cyber security; emerging trends and recent developments. Energy Rep. 2021;7:8176–86.

Article  Google Scholar 

Sethuraman SC, Vijayakumar V, Walczak S. Cyber attacks on healthcare devices using unmanned aerial vehicles. J Med Syst. 2020;44(1):29.

Article  Google Scholar 

Right to privacy as a fundamental right.pdf. https://loksabhadocs.nic.in/Refinput/NewReferenceNotes/English/Right%20to%20Privacy%20as%20a%20fundamental%20Right.pdf. Accessed 23 Jun 2023.

Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR). 2021;54(6):1–35.

Article  Google Scholar 

Hellstro¨m T, Dignum V, Bensch S. Bias in machine learning– what is it good for? arXiv preprint. http://arxiv.org/abs/2004.00686. 2020.

Study finds a potential risk with self-driving cars: failure to detect dark-skinned pedestrians - vox. https://www.vox.com/future-perfect/2019/3/5/18251924/self-driving-car-racial-bias-study-autonomous-vehicle-dark-skin. Accessed 23 Jun 2023.

Grover H, Alladi T, Chamola V, Singh D, Choo KK. Edge computing and deep learning enabled secure multitier network for internet of vehicles. IEEE Internet Things J. 2021;8(19):14787–14796.

Zhou Z-H, Machine learning. Springer Nature. 2021.

Mitchell TM, et al. Machine learning. McGraw-hill New York. 2007;1.

El Naqa I, Murphy MJ. What is machine learning? Springer. 2015.

Bottou L. Stochastic gradient descent tricks. Neural Networks: Tricks of the Trade: Second Edition. 2012;421–436.

Kerr P. Adaptive learning. ELT J. 2016;70(1):88–93.

Article  Google Scholar 

Gupta V, Jung C, Neel S, Roth A, Sharifi-Malvajerdi S, Waites C. Adaptive machine unlearning. Adv Neural Inf Process Sys. 2021;34:16319–16 330.

Ruder S. An overview of gradient descent optimization algorithms. arXiv preprint.http://arxiv.org/abs/1609.04747. 2016.

Melnikov Y. Influence functions and matrices. CRC Press. 1998;119.

Ketkar N, Ketkar N. Stochastic gradient descent. Deep learning with Python: a hands-on introduction. 2017;113–132.

Tahiliani A, Hassija V, Chamola V, Guizani M. Machine unlearning: its need and implementation strategies, in 2021 Thirteenth International Conference on Contemporary Computing (IC3–2021), ser. IC3 ’21. New York, NY, USA: association for computing machinery. 2021;241–246. [Online]. Available: https://doi.org/10.1145/3474124.3474158.

Sekhari A, Acharya J, Kamath G, Suresh AT. Remember what you want to forget: algorithms for machine unlearning. Advances in Neural Information Processing Systems. 2021;34: 18075–18086.

Gill PE, Murray W, Wright MH. Practical optimization. SIAM. 2019.

Bourtoule L, Chandrasekaran V, Choquette-Choo CA, Jia H, Travers A, Zhang B, Lie D, Papernot N, Machine unlearning, in,. IEEE Symposium on Security and Privacy (SP). IEEE. 2021;2021:141–59.

Google Scholar 

Warnecke A, Pirch L, Wressnegger C, Rieck K. Machine unlearning of features and labels. arXiv preprint. http://arxiv.org/abs/2108.11577. 2021.

Welsch RE. Influence functions and regression diagnostics, in Modern data analysis. Elsevier. 1982;149–169.

Covert I, Lundberg S, Lee S-I. Feature removal is a unifying principle for model explanation methods. arXiv preprint. http://arxiv.org/abs/2011.03623. 2020.

Van Dyk DA, Meng X-L. The art of data augmentation. J Comput Graph Stat. 2001;10(1):1–50.

Parisi GI, Kemker R, Part JL, Kanan C, Wermter S. https://www.businessinsider.in/tech/news/it-ministry-orders-vpn-providers-to-store-user-data-for-fiveyears-tech-news/articleshow/91334830.cms. Accessed 23 Jun 2023.

Allison B, Guthrie D, Guthrie L. Another look at the data sparsity problem. InText, Speech and Dialogue: 9th International Conference, TSD 2006, Brno, Czech Republic, September 11-15, 2006. Proceedings 9. Springer. 2006;327–34.

Zhang Y, Yang Q. An overview of multi-task learning. Natl Sci Rev. 2018;5(1):30–43.

Article  Google Scholar 

Laal M, Salamati P. Lifelong learning; why do we need it? Procedia Soc Behav Sci. 2012;31:399–403.

Article  Google Scholar 

Liu B, Liu Q, Stone P. Continual learning and private unlearning. arXiv preprint. http://arxiv.org/abs/2203.12817. 2022.

Nguyen TT, Duong CT, Weidlich M, Yin H, Nguyen QVH. Retaining data from streams of social platforms with minimal regret, in Twenty-sixth International Joint Conference on Artificial Intelligence, no. CONF. 2017.

Huang H, Ma X, Erfani SM, Bailey J, Wang Y. Unlearnable examples: making personal data unexploitable. arXiv preprint. http://arxiv.org/abs/2101.04898. 2021.

Chundawat VS, Tarun AK, Mandal M, Kankanhalli M. Zeroshot machine unlearning. arXiv preprint. http://arxiv.org/abs/2201.05629. 2022.

Guo C, Goldstein T, Hannun A, Van Der Maaten L. Certified data removal from machine learning models. arXiv preprint. http://arxiv.org/abs/1911.03030. 2019.

Ginart A, Guan M, Valiant G, Zou JY. Making AI forget you: data deletion in machine learning. Adv Neural Inf Process Sys. 2019;32.

Brophy J, Lowd D. Machine unlearning for random forests, in International Conference on Machine Learning. PMLR. 2021;1092–1104.

Thudi A, Deza G, Chandrasekaran V, Papernot N, Unrolling sgd: understanding factors influencing machine unlearning, in,. IEEE 7th European Symposium on Security and Privacy (EuroS&P). IEEE. 2022;2022:303–19.

Google Scholar 

Neel S, Roth A, Sharifi-Malvajerdi S. Descent-to-delete: gradient-based methods for machine unlearning, in Algorithmic Learning Theory. PMLR. 2021;931–962.

Graves L, Nagisetty V, Ganesh V. Amnesiac machine learning, in Proceedings of the AAAI Conference on Artificial Intelligence. 2021;35(13):11516–11524.

Dwork C, Differential privacy: a survey of results, in International conference on theory and applications of models of computation. Springer. 2008;1–19.

Cao Y, Yang J, Towards making systems forget with machine unlearning, in,. IEEE Symposium on Security and Privacy. IEEE. 2015;2015:463–80.

Google Scholar 

Cauwenberghs G, Poggio T. Incremental and decremental support vector machine learning. Adv Neural Inf Process Sys. 2000;13.

Chen Y, Xiong J, Xu W, Zuo J. A novel online incremental and decremental learning algorithm based on variable support vector machine. Clust Comput. 2019;22(3):7435–45.

Article  Google Scholar 

Chundawat VS, Tarun AK, Mandal M, Kankanhalli M. Can bad teaching induce forgetting? Unlearning in deep networks using an incompetent teacher. arXiv preprint. http://arxiv.org/abs/2205.08096. 2022.

Schelter S, Grafberger S, Dunning T, Hedgecut: maintaining randomised trees for low-latency machine unlearning, in Proceedings of the 2021 International Conference on Management of Data. 2021;1545–1557.

Geurts P, Ernst D, Wehenkel L. Extremely randomized trees. Mach Learn. 2006;63(1):3–42.

Article  MATH  Google Scholar 

Golatkar A, Achille A, Soatto S. Forgetting outside the box: scrubbing deep networks of information accessible from input-output observations, in European Conference on Computer Vision. Springer. 2020; 383–398.

Golatkar A, Achille A, Ravichandran A, Polito M, Soatto S. Mixed-privacy forgetting in deep networks, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021;792–801.

Baumhauer T, Scho¨ttle P, Zeppelzauer M. Machine unlearning: linear filtration for logit-based classifiers. arXiv preprint. http://arxiv.org/abs/2002.02730. 2020.

Koch K, Soll M. No matter how you slice it: machine unlearning with sisa comes at the expense of minority classes, in 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML). IEEE. 2023;622–637.

Mahmud MS, Huang JZ, Salloum S, Emara TZ. and K. Sadat- diynov, A survey of data partitioning and sampling methods to support big data analysis, Big Data Mining and Analytics. 2020;3(2):85–101.

Google Scholar 

Picard RR, Berk KN. Data splitting. The American Statisti- cian. 1990;44(2):140–7.

Google Scholar 

Feng SY, Gangal V, Wei J, Chandar S, Vosoughi S, Mitamura T, Hovy E. A survey of data augmentation approaches for nlp. arXiv preprint. http://arxiv.org/abs/2105.03075. 2021.

Ul Hassan M, Rehmani MH, Rehan M, Chen J. Differential privacy in cognitive radio networks: a comprehensive survey. Cognitive Computation. 2022;1–36.

Szo¨re´nyi B. Characterizing statistical query learning: simplified notions and proofs, in International Conference on Algorithmic Learning Theory. Springer. 2009;186–200.

Yang K. New lower bounds for statistical query learning. J Comput Syst Sci. 2005;70(4):485–509.

Article  MathSciNet  MATH  Google Scholar 

Zhou Y, Huang K, Cheng C, Wang X, Hussain A, Liu X. Fastadabelief: improving convergence rate for belief-based adaptive optimizers by exploiting strong convexity. IEEE Transactions on Neural Networks and Learning Systems. 2022.

Ralambondrainy H. A conceptual version of the k-means algorithm. Pattern Recogn Lett. 1995;16(11):1147–57.

Article  Google Scholar 

Karasuyama M, Takeuchi I. Multiple incremental decremental learning of support vector machines. IEEE Trans Neural Networks. 2010;21(7):1048–59.

Article  Google Scholar 

Joyce JM. Kullback-leibler divergence, in International encyclope- dia of statistical science. Springer. 2011;720–722.

Clark LA, Pregibon D. Tree-based models, in Statistical models in S. Routledge. 2017;377–419.

Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD. An introduction to decision tree modeling. Journal of Chemometrics: A Journal of the Chemometrics Society. 2004;18(6):275–85.

Article  Google Scholar 

Spinelli I, Scardapane S, Hussain A, Uncini A. Biased edge dropout for enhancing fairness in graph representation learning. arXiv preprint. http://arxiv.org/abs/2104.14210. 2021.

Zhang Q, Zhong G, Dong J. A graph-based semi-supervised multi-label learning method based on label correlation consistency. Cogn Comput. 2021;13(6):1564–73.

Article  Google Scholar 

Miikkulainen R, Liang J, Meyerson E, Rawal A, Fink D, Fran- con O, Raju B, Shahrzad H, Navruzyan A, Duffy N, et al. Evolving deep neural networks, in Artificial intelligence in the age of neural networks and brain computing. Elsevier. 2019;293–312.

Agostinelli F, Hoffman M, Sadowski P, Baldi P. Learning activation functions to improve deep neural networks. arXiv preprint. http://arxiv.org/abs/1412.6830. 2014.

Chhikara P, Tekchandani R, Kumar N, Chamola V, Guizani M. Dcnn-ga: a deep neural net architecture for navigation of uav in indoor environment. IEEE Internet Things J. 2020;8(6):4448–60.

Article  Google Scholar 

Mahmud M, Kaiser MS, Hussain A, Vassanelli S. Applications of deep learning and reinforcement learning to biological data. IEEE transactions on neural networks and learning systems. 2018;29(6):2063–79.

Article  MathSciNet  Google Scholar 

Boyd SP, Vandenberghe L. Convex optimization. Cambridge university press. 2004.

Gao B, Pavel L. On the properties of the softmax function with application in game theory and reinforcement learning. arXiv preprint. http://arxiv.org/abs/1704.00805. 2017.

Freese F, et al. Testing accuracy. Forest Sci. 1960;6(2):139–45.

Google Scholar 

Hagenbach J, Koessler F. The Streisand effect: signaling and partial sophistication. J Econ Behav Organ. 2017;143:1–8.

Article  Google Scholar 

Swiler LP, Paez TL, Mayes RL. Epistemic uncertainty quantification tutorial, in Proceedings of the 27th International Modal Analysis Conference. 2009.

Carlini N, Chien S, Nasr M, Song S, Terzis A, Trame`r F, Membership inference attacks from first principles, CoRR, vol. abs/2112.03570, 2021. [Online]. Available: https://arxiv.org/abs/2112.03570.

Shokri R, Stronati M, Shmatikov V. Membership inference attacks against machine learning models, CoRR, vol. abs/1610.05820, 2016. [Online]. Available: http://arxiv.org/abs/1610.05820.

Shuvo MSR, Alhadidi D. Membership inference attacks: analysis and mitigation, in 2020 IEEE 19th International Conference on Trust. Security and Privacy in Computing and Communications (TrustCom). 2020;1410–1419.

Liu X, Xie L, Wang Y, Zou J, Xiong J, Ying Z, Vasilakos AV. Privacy and security issues in deep learning: a survey. IEEE Access. 2020;9:4566–93.

Article  Google Scholar 

Chundawat VS, Tarun AK, Mandal M, Kankanhalli M. Zeroshot machine unlearning. IEEE Transactions on Information Forensics and Security. 2023.

Wang K, Fu Y, Li K, Khisti A, Zemel RS, Makhzani A. Variational model inversion attacks, CoRR, vol. abs/2201.10787, 2022. [Online]. Available: https://arxiv.org/abs/2201.10787.

Fredrikson M. Jha S, Ristenpart T. Model inversion attacks that exploit confidence information and basic countermeasures, in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, ser. CCS ’15. New York, NY, USA: Association for Computing Machinery. 2015;1322–1333. [Online]. Available: https://doi.org/10.1145/2810103.2813677.

Xian Y, Lampert CH, Schiele B, Akata Z. Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans Pattern Anal Mach Intell. 2018;41(9):2251–65.

Article  Google Scholar 

Golatkar A, Achille A, Soatto S. Eternal sunshine of the spotless net: Selective forgetting in deep networks, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020;9304–9312.

Tarun AK, Chundawat VS, Mandal M, Kankanhalli M. Fast yet effective machine unlearning. arXiv preprint. http://arxiv.org/abs/2111.08947. 2021.

Becker A, Liebig T. Evaluating machine unlearning via epistemic uncertainty. arXiv preprint. http://arxiv.org/abs/2208.10836. 2022.

Wiedmann T, Minx J. A definition of ‘carbon footprint.’ Eco- logical economics research trends. 2008;1(2008):1–11.

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