research-article

Fairness Audit of Machine Learning Models with Confidential Computing

Authors:

Yeon-sup LimAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 3488 - 3499

https://doi.org/10.1145/3485447.3512244

Published: 25 April 2022 Publication History

Abstract

Algorithmic discrimination is one of the significant concerns in applying machine learning models to a real-world system. Many researchers have focused on developing fair machine learning algorithms without discrimination based on legally protected attributes. However, the existing research has barely explored various security issues that can occur while evaluating model fairness and verifying fair models. In this study, we propose a fairness audit framework that assesses the fairness of ML algorithms while addressing potential security issues such as data privacy, model secrecy, and trustworthiness. To this end, our proposed framework utilizes confidential computing and builds a chain of trust through enclave attestation primitives combined with public scrutiny and state-of-the-art software-based security techniques, enabling fair ML models to be securely certified and clients to verify a certified one. Our micro-benchmarks on various ML models and real-world datasets show the feasibility of the fairness certification implemented with Intel SGX in practice. In addition, we analyze the impact of data poisoning, which is an additional threat during data collection for fairness auditing. Based on the analysis, we illustrate the theoretical curves of fairness gap and minimal group size and the empirical results of fairness certification on poisoned datasets.

References

[1]

[n. d.]. Confidential Computing Consortium. Available at https://confidentialcomputing.io/ (2022/02/23).

[2]

Carlos Affonso, André Luis Debiaso Rossi, Fábio Henrique Antunes Vieira, André Carlos Ponce de Leon Ferreira, 2017. Deep learning for biological image classification. Expert Systems with Applications 85 (2017), 114–122.

Digital Library

[3]

Alekh Agarwal, Miroslav Dudík, and Zhiwei Steven Wu. 2019. Fair regression: Quantitative definitions and reduction-based algorithms. In International Conference on Machine Learning. PMLR, 120–129.

[4]

Ittai Anati, Shay Gueron, Simon Johnson, and Vincent Scarlata. 2013. Innovative technology for CPU based attestation and sealing. In Proceedings of the 2nd international workshop on hardware and architectural support for security and privacy, Vol. 13. Citeseer, 7.

[5]

McKane Andrus, Elena Spitzer, Jeffrey Brown, and Alice Xiang. 2020. “What We Can’t Measure, We Can’t Understand”: Challenges to Demographic Data Procurement in the Pursuit of Fairness. arXiv preprint arXiv:2011.02282(2020).

[6]

Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. ProPublica. See https://www. propublica. org/article/machine-bias-risk-assessments-in-criminal-sentencing(2016).

[7]

Peter Arcidiacono, Shakeeb Khan, and Jacob L Vigdor. 2011. Representation versus assimilation: How do preferences in college admissions affect social interactions?Journal of Public Economics 95, 1-2 (2011), 1–15.

[8]

ARM. 2009. ARM Security Technology – Building a Secure System using TrustZone Technology. http://infocenter.arm.com/help/topic/com.arm.doc.prd29-genc-009492c/PRD29-GENC-009492C_trustzone_security_whitepaper.pdf.

[9]

Solon Barocas and Andrew D Selbst. 2016. Big data’s disparate impact. Calif. L. Rev. 104(2016), 671.

[10]

Andrew Baumann, Marcus Peinado, and Galen Hunt. 2015. Shielding applications from an untrusted cloud with haven. ACM Transactions on Computer Systems (TOCS) 33, 3 (2015), 1–26.

Digital Library

[11]

Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, 2018. AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943(2018).

[12]

Sarah Bird, Miro Dudík, Richard Edgar, Brandon Horn, Roman Lutz, Vanessa Milan, Mehrnoosh Sameki, Hanna Wallach, and Kathleen Walker. 2020. Fairlearn: A toolkit for assessing and improving fairness in AI. Microsoft, Tech. Rep. MSR-TR-2020-32(2020).

[13]

Flavio P Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney. 2017. Optimized pre-processing for discrimination prevention. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 3995–4004.

[14]

Chia che Tsai, Donald E. Porter, and Mona Vij. 2017. Graphene-SGX: A Practical Library OS for Unmodified Applications on SGX. In 2017 USENIX Annual Technical Conference (USENIX ATC 17). USENIX Association, Santa Clara, CA, 645–658. https://www.usenix.org/conference/atc17/technical-sessions/presentation/tsai

Digital Library

[15]

Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5, 2 (2017), 153–163.

[16]

Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq. 2017. Algorithmic decision making and the cost of fairness. In Proceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining. 797–806.

Digital Library

[17]

Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. http://archive.ics.uci.edu/ml

[18]

Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. 2015. Certifying and removing disparate impact. In proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. 259–268.

Digital Library

[19]

Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. 2015. Model inversion attacks that exploit confidence information and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 1322–1333.

Digital Library

[20]

Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572(2014).

[21]

Karan Grover, Shruti Tople, Shweta Shinde, Ranjita Bhagwan, and Ramachandran Ramjee. 2018. Privado: Practical and secure DNN inference with enclaves. arXiv preprint arXiv:1810.00602(2018).

[22]

Zhongshu Gu, Heqing Huang, Jialong Zhang, Dong Su, Ankita Lamba, Dimitrios Pendarakis, and Ian Molloy. 2018. Securing input data of deep learning inference systems via partitioned enclave execution. arXiv preprint arXiv:1807.00969(2018).

[23]

Juhyeng Han, Seongmin Kim, Daeyang Cho, Byungkwon Choi, Jaehyeong Ha, and Dongsu Han. 2020. A secure middlebox framework for enabling visibility over multiple encryption protocols. IEEE/ACM Transactions on Networking 28, 6 (2020), 2727–2740.

Digital Library

[24]

Lucjan Hanzlik, Yang Zhang, Kathrin Grosse, Ahmed Salem, Maximilian Augustin, Michael Backes, and Mario Fritz. 2021. Mlcapsule: Guarded offline deployment of machine learning as a service. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3300–3309.

[25]

Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of Opportunity in Supervised Learning. In NIPS.

[26]

Matthew Hoekstra, Reshma Lal, Pradeep Pappachan, Vinay Phegade, and Juan Del Cuvillo. 2013. Using innovative instructions to create trustworthy software solutions. HASP@ ISCA 11, 10.1145, 2487726–2488370.

[27]

Tyler Hunt, Congzheng Song, Reza Shokri, Vitaly Shmatikov, and Emmett Witchel. 2018. Chiron: Privacy-preserving machine learning as a service. arXiv preprint arXiv:1803.05961(2018).

[28]

Tyler Hunt, Zhiting Zhu, Yuanzhong Xu, Simon Peter, and Emmett Witchel. 2016. Ryoan: A Distributed Sandbox for Untrusted Computation on Secret Data. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, Savannah, GA, 533–549. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/hunt

Digital Library

[29]

Kimmo Kärkkäinen and Jungseock Joo. 2019. Fairface: Face attribute dataset for balanced race, gender, and age. arXiv preprint arXiv:1908.04913(2019).

[30]

Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. 2018. Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. In International Conference on Machine Learning. PMLR, 2564–2572.

[31]

Niki Kilbertus, Adrià Gascón, Matt Kusner, Michael Veale, Krishna Gummadi, and Adrian Weller. 2018. Blind justice: Fairness with encrypted sensitive attributes. In International Conference on Machine Learning. PMLR, 2630–2639.

[32]

Kyungtae Kim, Chung Hwan Kim, Junghwan” John” Rhee, Xiao Yu, Haifeng Chen, Dave Tian, and Byoungyoung Lee. 2020. Vessels: efficient and scalable deep learning prediction on trusted processors. In Proceedings of the 11th ACM Symposium on Cloud Computing. 462–476.

Digital Library

[33]

Seongmin Kim, Juhyeng Han, Jaehyeong Ha, Taesoo Kim, and Dongsu Han. 2018. Sgx-tor: A secure and practical tor anonymity network with sgx enclaves. IEEE/ACM Transactions on Networking 26, 5 (2018), 2174–2187.

Digital Library

[34]

Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2016. Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807(2016).

[35]

Roland Kunkel, Do Le Quoc, Franz Gregor, Sergei Arnautov, Pramod Bhatotia, and Christof Fetzer. 2019. Tensorscone: A secure tensorflow framework using intel sgx. arXiv preprint arXiv:1902.04413(2019).

[36]

Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed H Chi. 2020. Fairness without demographics through adversarially reweighted learning. arXiv preprint arXiv:2006.13114(2020).

[37]

Andrew Law, Chester Leung, Rishabh Poddar, Raluca Ada Popa, Chenyu Shi, Octavian Sima, Chaofan Yu, Xingmeng Zhang, and Wenting Zheng. 2020. Secure collaborative training and inference for xgboost. In Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice. 21–26.

Digital Library

[38]

Sungyoon Lee, Jaewook Lee, and Saerom Park. 2020. Lipschitz-certifiable training with a tight outer bound. Advances in Neural Information Processing Systems 33 (2020).

[39]

Taegyeong Lee, Zhiqi Lin, Saumay Pushp, Caihua Li, Yunxin Liu, Youngki Lee, Fengyuan Xu, Chenren Xu, Lintao Zhang, and Junehwa Song. 2019. Occlumency: Privacy-preserving remote deep-learning inference using sgx. In The 25th Annual International Conference on Mobile Computing and Networking. 1–17.

Digital Library

[40]

Gilles Louppe, Michael Kagan, and Kyle Cranmer. 2017. Learning to Pivot with Adversarial Networks. In Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Vol. 30. Curran Associates, Inc.https://proceedings.neurips.cc/paper/2017/file/48ab2f9b45957ab574cf005eb8a76760-Paper.pdf

[41]

Natalia L Martinez, Martin A Bertran, Afroditi Papadaki, Miguel Rodrigues, and Guillermo Sapiro. 2021. Blind Pareto Fairness and Subgroup Robustness. In International Conference on Machine Learning. PMLR, 7492–7501.

[42]

Maw Maw, Su-Cheng Haw, and Chin-Kuan Ho. 2021. Utilizing data sampling techniques on algorithmic fairness for customer churn prediction with data imbalance problems. F1000Research 10, 988 (2021), 988.

[43]

Marcin Michał Mirończuk and Jarosław Protasiewicz. 2018. A recent overview of the state-of-the-art elements of text classification. Expert Systems with Applications 106 (2018), 36–54.

[44]

Fan Mo, Hamed Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, and Nicolas Kourtellis. 2021. PPFL: privacy-preserving federated learning with trusted execution environments. arXiv preprint arXiv:2104.14380(2021).

[45]

Alfred Ng. [n. d.]. Can Auditing Eliminate Bias from Algorithms?Available at https://themarkup.org/ask-the-markup/2021/02/23/can-auditing-eliminate-bias-from-algorithms(2021/02/23).

[46]

Olga Ohrimenko, Felix Schuster, Cédric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, and Manuel Costa. 2016. Oblivious multi-party machine learning on trusted processors. In 25th {USENIX} Security Symposium ({USENIX} Security 16). 619–636.

[47]

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Eds.). Curran Associates, Inc., 8024–8035. http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf

Digital Library

[48]

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825–2830.

Digital Library

[49]

Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger. 2017. On fairness and calibration. arXiv preprint arXiv:1709.02012(2017).

[50]

Christian Priebe, Divya Muthukumaran, Joshua Lind, Huanzhou Zhu, Shujie Cui, Vasily A Sartakov, and Peter Pietzuch. 2019. SGX-LKL: Securing the host OS interface for trusted execution. arXiv preprint arXiv:1908.11143(2019).

[51]

Do Le Quoc, Franz Gregor, Sergei Arnautov, Roland Kunkel, Pramod Bhatotia, and Christof Fetzer. 2020. secureTF: a secure TensorFlow framework. In Proceedings of the 21st International Middleware Conference. 44–59.

Digital Library

[52]

Manish Raghavan, Solon Barocas, Jon Kleinberg, and Karen Levy. 2020. Mitigating bias in algorithmic hiring: Evaluating claims and practices. In Proceedings of the 2020 conference on fairness, accountability, and transparency. 469–481.

Digital Library

[53]

Pedro Saleiro, Benedict Kuester, Loren Hinkson, Jesse London, Abby Stevens, Ari Anisfeld, Kit T Rodolfa, and Rayid Ghani. 2018. Aequitas: A bias and fairness audit toolkit. arXiv preprint arXiv:1811.05577(2018).

[54]

Shahar Segal, Yossi Adi, Benny Pinkas, Carsten Baum, Chaya Ganesh, and Joseph Keshet. 2021. Fairness in the eyes of the data: Certifying machine-learning models. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. 926–935.

Digital Library

[55]

Hossein Shafagh, Anwar Hithnawi, Andreas Dröscher, Simon Duquennoy, and Wen Hu. 2015. Talos: Encrypted query processing for the internet of things. In Proceedings of the 13th ACM conference on embedded networked sensor systems. 197–210.

Digital Library

[56]

Fahad Shaon, Murat Kantarcioglu, Zhiqiang Lin, and Latifur Khan. 2017. Sgx-bigmatrix: A practical encrypted data analytic framework with trusted processors. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 1211–1228.

Digital Library

[57]

Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017. Membership inference attacks against machine learning models. In 2017 IEEE Symposium on Security and Privacy (SP). IEEE, 3–18.

[58]

Florian Tramer and Dan Boneh. 2018. Slalom: Fast, verifiable and private execution of neural networks in trusted hardware. arXiv preprint arXiv:1806.03287(2018).

[59]

Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart. 2016. Stealing machine learning models via prediction apis. In 25th {USENIX} Security Symposium ({USENIX} Security 16). 601–618.

[60]

Michael Veale and Reuben Binns. 2017. Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data. Big Data & Society 4, 2 (2017), 2053951717743530.

[61]

Bennet Yee, David Sehr, Gregory Dardyk, J Bradley Chen, Robert Muth, Tavis Ormandy, Shiki Okasaka, Neha Narula, and Nicholas Fullagar. 2009. Native client: A sandbox for portable, untrusted x86 native code. In 2009 30th IEEE Symposium on Security and Privacy. IEEE, 79–93.

Digital Library

[62]

Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi. 2017. Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th international conference on world wide web. 1171–1180.

Digital Library

[63]

Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P Gummadi. 2019. Fairness constraints: A flexible approach for fair classification. The Journal of Machine Learning Research 20, 1 (2019), 2737–2778.

Digital Library

[64]

Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018. Mitigating unwanted biases with adversarial learning. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. 335–340.

Digital Library

[65]

Chengliang Zhang, Junzhe Xia, Baichen Yang, Huancheng Puyang, Wei Wang, Ruichuan Chen, Istemi Ekin Akkus, Paarijaat Aditya, and Feng Yan. 2021. Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX. arXiv preprint arXiv:2105.01281(2021).

[66]

Xiaoli Zhang, Fengting Li, Zeyu Zhang, Qi Li, Cong Wang, and Jianping Wu. 2020. Enabling execution assurance of federated learning at untrusted participants. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications. IEEE, 1877–1886.

Digital Library

[67]

Jianping Zhu, Rui Hou, XiaoFeng Wang, Wenhao Wang, Jiangfeng Cao, Boyan Zhao, Zhongpu Wang, Yuhui Zhang, Jiameng Ying, Lixin Zhang, 2020. Enabling rack-scale confidential computing using heterogeneous trusted execution environment. In 2020 IEEE Symposium on Security and Privacy (SP). IEEE, 1450–1465.

Cited By

Xue JZheng MSheng YYang LLou QJiang LLi BXu WChen JZhang YXue JWang SBai GYuan X(2024)TrojFair: Trojan Fairness AttacksProceedings of the 1st ACM Workshop on Large AI Systems and Models with Privacy and Safety Analysis10.1145/3689217.3690620(47-56)Online publication date: 19-Nov-2024
https://dl.acm.org/doi/10.1145/3689217.3690620
Mo FTarkhani ZHaddadi H(2024)Machine Learning with Confidential Computing: A Systematization of KnowledgeACM Computing Surveys10.1145/367000756:11(1-40)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1145/3670007
Laine JMinkkinen MMäntymäki M(2024)Ethics-based AI auditingInformation and Management10.1016/j.im.2024.10396961:5Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.im.2024.103969
Show More Cited By

Index Terms

Fairness Audit of Machine Learning Models with Confidential Computing

Index terms have been assigned to the content through auto-classification.

Recommendations

Airtime Fairness for IEEE 802.11 Multirate Networks

Under a multi rate network scenario, the IEEE 802.11 DCF MAC fails to provide air-time fairness for all competing stations since the protocol is designed for ensuring max-min throughput fairness and the maximum achievable throughput by any station gets ...
Fairness in multi-hop wireless backhaul networks: a dynamic estimation approach
QShine '08: Proceedings of the 5th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness

In this work, we consider the problem of fairness for Transit Access Points (TAP) in multi-hop wireless backhaul networks. Existing approaches are not practical due to the requirement for modifications to the MAC layer or queueing operations of TAPs, or ...
Dynamic Contention Window Control Mechanism to Achieve Fairness between Uplink and Downlink Flows in IEEE 802.11 Wireless LANs

This paper considers a fairness issue between uplink and downlink flows in IEEE 802.11 wireless LANs, where uplink flows dominate over downlink flows in terms of wireless bandwidth usage. As a solution to this unfairness, we propose modifying the IEEE ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 April 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

National Research Foundation of Korea(NRF)
National Research Foundation of Korea(NRF)

Conference

WWW '22

Sponsor:

SIGWEB

WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
714
Total Downloads

Downloads (Last 12 months)161
Downloads (Last 6 weeks)21

Reflects downloads up to 20 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Xue JZheng MSheng YYang LLou QJiang LLi BXu WChen JZhang YXue JWang SBai GYuan X(2024)TrojFair: Trojan Fairness AttacksProceedings of the 1st ACM Workshop on Large AI Systems and Models with Privacy and Safety Analysis10.1145/3689217.3690620(47-56)Online publication date: 19-Nov-2024
https://dl.acm.org/doi/10.1145/3689217.3690620
Mo FTarkhani ZHaddadi H(2024)Machine Learning with Confidential Computing: A Systematization of KnowledgeACM Computing Surveys10.1145/367000756:11(1-40)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1145/3670007
Laine JMinkkinen MMäntymäki M(2024)Ethics-based AI auditingInformation and Management10.1016/j.im.2024.10396961:5Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.im.2024.103969
Duddu VDas AKhayata NYalame HSchneider TAsokan N(2024)Attesting Distributional Properties of Training Data for Machine LearningComputer Security – ESORICS 202410.1007/978-3-031-70879-4_1(3-23)Online publication date: 5-Sep-2024
https://doi.org/10.1007/978-3-031-70879-4_1
Freiberger VBuchmann E(2024)Fairness Certification for Natural Language Processing and Large Language ModelsIntelligent Systems and Applications10.1007/978-3-031-66329-1_39(606-624)Online publication date: 31-Jul-2024
https://doi.org/10.1007/978-3-031-66329-1_39
Will NMaziero C(2023)Intel Software Guard Extensions Applications: A SurveyACM Computing Surveys10.1145/359302155:14s(1-38)Online publication date: 17-Jul-2023
https://dl.acm.org/doi/10.1145/3593021
Karan ABalepur NSundaram H(2023)Your Browsing History May Cost You: A Framework for Discovering Differential Pricing in Non-Transparent MarketsProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594038(717-735)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3593013.3594038
Lo D(2023)Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE)10.1109/ICSE-FoSE59343.2023.00010(69-85)Online publication date: 14-May-2023
https://doi.org/10.1109/ICSE-FoSE59343.2023.00010
Kolberg J(2023)Fairness von Biometrischen SystemenDatenschutz und Datensicherheit - DuD10.1007/s11623-022-1709-147:1(15-21)Online publication date: 2-Jan-2023
https://doi.org/10.1007/s11623-022-1709-1
Toreini EMehrnezhad Mvan Moorsel A(2023)Fairness as a Service (FaaS): verifiable and privacy-preserving fairness auditing of machine learning systemsInternational Journal of Information Security10.1007/s10207-023-00774-z23:2(981-997)Online publication date: 7-Nov-2023
https://dl.acm.org/doi/10.1007/s10207-023-00774-z
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents