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https://github.com/ZhengyuZhao/AI-Security-and-Privacy-Events

A curated list of academic events on AI Security & Privacy
https://github.com/ZhengyuZhao/AI-Security-and-Privacy-Events
adversarial-examples adversarial-machine-learning ai-privacy ai-security data-poisoning
Last synced: 9 days ago
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A curated list of academic events on AI Security & Privacy
Host: GitHub
URL: https://github.com/ZhengyuZhao/AI-Security-and-Privacy-Events
Owner: ZhengyuZhao
License: mit
Created: 2021-10-04T11:57:58.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-08-22T06:20:57.000Z (3 months ago)
Last Synced: 2024-08-22T07:34:30.066Z (3 months ago)
Topics: adversarial-examples, adversarial-machine-learning, ai-privacy, ai-security, data-poisoning
Homepage:
Size: 118 KB
Stars: 124
Watchers: 7
Forks: 15
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
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README

        # A curated list of AI Security & Privacy academic events

## Seminar

  + [**NLP & LLM Security**](https://sig.llmsecurity.net/talks/)

  + [**Privacy and Security in ML (PriSec-ML)**](https://prisec-ml.github.io/)

  + [**Machine Learning Security (MLSec)**](https://www.youtube.com/c/MLSec/playlists)

  + [**Seminars on Security & Privacy in Machine Learning (ML S&P)**](https://vsehwag.github.io/SPML_seminar/)  

  + [**AI Security and Privacy (AISP)**](https://space.bilibili.com/1556922191/?spm_id_from=333.999.0.0) (in Chinese)

## Conference

  + [**IEEE Conference on Secure and Trustworthy Machine Learning (2022-)**](https://satml.org/)

  + [**The Conference on Applied Machine Learning in Information Security (2017-)**](https://www.camlis.org/)

## Workshop

- ### Security & Privacy

  + **Artificial Intelligence and Security** ([CCS 2008-](https://aisec.cc/))

  + **Deep Learning Security and Privacy** ([S&P 2018-](https://dlsp2024.ieee-security.org/))

  + **Dependable and Secure Machine Learning** ([DSN 2018-](https://dependablesecureml.github.io/index.html))

  + **Security Architectures for Generative-AI Systems** ([S&P 2024](https://sites.google.com/view/sagai2024/home))

  + **AI System with Confidential Computing** ([NDSS 2024](https://sites.google.com/view/aiscc2024))

- ### Machine Learning & Artificial Intelligence

  + **Red Teaming GenAI: What Can We Learn from Adversaries?** ([NeurIPS 2024](https://redteaming-gen-ai.github.io/))

  + **Safe Generative AI** ([NeurIPS 2024](https://safegenaiworkshop.github.io/))

  + **Towards Safe & Trustworthy Agents** ([NeurIPS 2024](https://www.mlsafety.org/events/neurips/2024))

  + **Socially Responsible Language Modelling Research** ([NeurIPS 2024](https://solar-neurips.github.io/))

  + **Next Generation of AI Safety** ([ICML 2024](https://icml-nextgenaisafety.github.io/))

  + **Trustworthy Multi-modal Foundation Models and AI Agents** ([ICML 2024](https://icml-tifa.github.io/))

  + **Secure and Trustworthy Large Language Models** ([ICLR 2024](https://set-llm.github.io/))

  + **Reliable and Responsible Foundation Models** ([ICLR 2024](https://iclr-r2fm.github.io/))

  + **Privacy Regulation and Protection in Machine Learning** ([ICLR 2024](https://pml-workshop.github.io/iclr24/))

  + **Responsible Language Models** ([AAAI 2024](https://sites.google.com/vectorinstitute.ai/relm2024/home))

  + **Privacy-Preserving Artificial Intelligence** ([AAAI 2020-2024](https://aaai-ppai24.github.io/))

  + **Practical Deep Learning in the Wild** ([CAI 2024, AAAI 2022-2023](https://practical-dl.github.io/))

  + **Backdoors in Deep Learning: The Good, the Bad, and the Ugly** ([NeurIPS 2023](https://neurips2023-bugs.github.io/))

  + **Trustworthy and Reliable Large-Scale Machine Learning Models** ([ICLR 2023](https://rtml-iclr2023.github.io/))

  + **Backdoor Attacks and Defenses in Machine Learning** ([ICLR 2023](https://iclr23-bands.github.io/))

  + **Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data** ([ICLR 2022](https://pair2struct-workshop.github.io/))

  + **Security and Safety in Machine Learning Systems** ([ICLR 2021](https://aisecure-workshop.github.io/aml-iclr2021/))

  + **Robust and Reliable Machine Learning in the Real World** ([ICLR 2021](https://sites.google.com/connect.hku.hk/robustml-2021/home))

  + **Towards Trustworthy ML: Rethinking Security and Privacy for ML** ([ICLR 2020](https://trustworthyiclr20.github.io/))

  + **Safe Machine Learning: Specification, Robustness and Assurance** ([ICLR 2019](https://sites.google.com/view/safeml-iclr2019))

  + **New Frontiers in Adversarial Machine Learning** ([ICML 2022-2023](https://advml-frontier.github.io/)) 

  + **Theory and Practice of Differential Privacy** ([ICML 2021-2022](https://tpdp.journalprivacyconfidentiality.org/2022/)) 

  + **Uncertainty & Robustness in Deep Learning** ([ICML 2020-2021](https://sites.google.com/view/udlworkshop2021/home))

  + **A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning** ([ICML 2021](https://advml-workshop.github.io/icml2021/))

  + **Security and Privacy of Machine Learning** ([ICML 2019](https://icml2019workshop.github.io/)) 

  + **Socially Responsible Machine Learning** ([NeurIPS 2022](https://tsrml2022.github.io/), [ICLR 2022](https://iclrsrml.github.io/), [ICML 2021](https://icmlsrml2021.github.io/))

  + **ML Safety** ([NeurIPS 2022](https://neurips2022.mlsafety.org/))

  + **Privacy in Machine Learning** ([NeurIPS 2021](https://priml2021.github.io/))

  + **Dataset Curation and Security** ([NeurIPS 2020](http://securedata.lol/))

  + **Security in Machine Learning** ([NeurIPS 2018](https://secml2018.github.io/))

  + **Machine Learning and Computer Security** ([NeurIPS 2017](https://machine-learning-and-security.github.io/))

  + **Adversarial Training** ([NeurIPS 2016](https://sites.google.com/site/nips2016adversarial/))

  + **Reliable Machine Learning in the Wild** ([NeurIPS 2016](https://sites.google.com/site/wildml2016nips/home))

  + **Adversarial Learning Methods for Machine Learning and Data Mining** ([KDD 2019-2022](https://sites.google.com/view/advml))

  + **Privacy Preserving Machine Learning** ([FOCS 2022, CCS 2021, NeurIPS 2020, CCS 2019, NeurIPS 2018](https://ppml-workshop.github.io/))

  + **SafeAI** ([AAAI 2019-2022](https://safeai.webs.upv.es/))

  + **Adversarial Machine Learning and Beyond** ([AAAI 2022](https://advml-workshop.github.io/aaai2022/))

  + **Towards Robust, Secure and Efficient Machine Learning** ([AAAI2021](http://federated-learning.org/rseml2021/))

  + **AISafety** ([IJCAI 2019-2022](https://www.aisafetyw.org/))

- ### Computer Vision

  + **The Dark Side of Generative AIs and Beyond** ([ECCV 2024](https://privacy-preserving-computer-vision.github.io/eccv24.html))

  + **Trust What You learN** ([ECCV 2024](https://twyn.unimore.it/))

  + **Privacy for Vision & Imaging** ([ECCV 2024](https://sites.google.com/view/rcv-at-eccv-2022/home?authuser=0))

  + **Adversarial Machine Learning on Computer Vision** ([CVPR 2024](https://cvpr24-advml.github.io/), [CVPR 2023](https://robustart.github.io/), [CVPR 2022](https://artofrobust.github.io/), [CVPR 2020](https://adv-workshop-2020.github.io/))

  + **Secure and Safe Autonomous Driving** ([CVPR 2023](https://ai-secure.github.io/SSAD2023/))

  + **Adversarial Robustness in the Real World** ([ICCV 2023](https://iccv23-arow.github.io/), [ECCV 2022](https://eccv22-arow.github.io/), [ICCV 2021](https://iccv21-adv-workshop.github.io/), [CVPR 2021](https://aisecure-workshop.github.io/amlcvpr2021/), [ECCV 2020](https://eccv20-adv-workshop.github.io/), [CVPR 2020](https://adv-workshop-2020.github.io/), [CVPR 2019](https://amlcvpr2019.github.io/))

  + **The Bright and Dark Sides of Computer Vision: Challenges and Opportunities for Privacy and Security** ([CVPR 2021](https://quovadiscvpr.cispa.de/), [ECCV 2020](https://cvcops20.cispa.saarland/), [CVPR 2019](https://cvcops19.cispa.saarland/), [CVPR 2018](https://vision.soic.indiana.edu/bright-and-dark-workshop-2018/), [CVPR 2017](https://vision.soic.indiana.edu/bright-and-dark-workshop-2017/))

  + **Responsible Computer Vision** ([ECCV 2022](https://sites.google.com/view/rcv-at-eccv-2022/home?authuser=0))

  + **Safe Artificial Intelligence for Automated Driving** ([ECCV 2022](https://sites.google.com/view/saiad2022))

  + **Adversarial Learning for Multimedia** ([ACMMM 2021](https://advm-workshop-2021.github.io/))

  + **Adversarial Machine Learning towards Advanced Vision Systems** ([ACCV 2022](https://sites.google.com/view/workshop-of-amlavs))

- ### Natural Language Processing

  + **Trustworthy Natural Language Processing** ([2021-2024](https://trustnlpworkshop.github.io/))

  + **Privacy in Natural Language Processing** ([ACL 2024](https://sites.google.com/view/privatenlp/), [NAACL 2022](https://sites.google.com/view/privatenlp/home/naacl-2022), [NAACL 2021](https://sites.google.com/view/privatenlp/home/naacl-2021), [EMNLP 2020](https://sites.google.com/view/privatenlp/home/emnlp-2020), [WSDM 2020](https://sites.google.com/view/privatenlp/home/wsdm-2020))

  + **BlackboxNLP** ([2018-2024](https://blackboxnlp.github.io/))

  

- ### Information Retrieval

  + **Online Misinformation- and Harm-Aware Recommender Systems** ([RecSys 2021](https://ohars-recsys.isistan.unicen.edu.ar/topics-of-interest), [RecSys 2020](https://ohars-recsys2020.isistan.unicen.edu.ar/))

  + **Adversarial Machine Learning for Recommendation and Search** ([CIKM 2021](https://sisinflab.github.io/adverse2021/))

## Tutorial  

- ### Machine Learning & Artificial Intelligence

  + **Quantitative Reasoning About Data Privacy in Machine Learning** ([ICML 2022](https://icml.cc/Conferences/2022/Schedule?showEvent=18439))

  + **Foundational Robustness of Foundation Models** ([NeurIPS 2022](https://sites.google.com/view/neurips2022-frfm-turotial/home))

  + **Adversarial Robustness - Theory and Practice** ([NeurIPS 2018](https://adversarial-ml-tutorial.org/))

  + **Towards Adversarial Learning: from Evasion Attacks to Poisoning Attacks** ([KDD 2022](https://sites.google.com/view/kdd22-tutorial-adv-learn/))

  + **Adversarial Robustness in Deep Learning: From Practices to Theories** ([KDD 2021](https://sites.google.com/view/kdd21-tutorial-adv-robust/))

  + **Adversarial Attacks and Defenses: Frontiers, Advances and Practice** ([KDD 2020](https://sites.google.com/view/kdd-2020-attack-and-defense/home))

  + **Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications** ([ICDM 2020](https://tutorial.trustdeeplearning.com/))

  + **Adversarial Machine Learning for Good** ([AAAI 2022](https://sites.google.com/view/advml4good))

  + **Adversarial Machine Learning** ([AAAI 2018](https://aaai18adversarial.github.io/index.html#syl))

- ### Computer Vision

  + **Adversarial Machine Learning in Computer Vision** ([CVPR 2021](https://advmlincv.github.io/cvpr21-tutorial/))

  + **Practical Adversarial Robustness in Deep Learning: Problems and Solutions** ([CVPR 2021](https://sites.google.com/view/par-2021))

  + **Adversarial Robustness of Deep Learning Models** ([ECCV 2020](https://sites.google.com/umich.edu/eccv-2020-adv-robustness))

  + **Deep Learning for Privacy in Multimedia** ([ACMMM 2020](http://cis.eecs.qmul.ac.uk/privacymultimedia.html))

 

- ### Natural Language Processing

  + **Vulnerabilities of Large Language Models to Adversarial Attacks** ([ACL 2024](https://llm-vulnerability.github.io/))

  + **Robustness and Adversarial Examples in Natural Language Processing** ([EMNLP 2021](https://2021.emnlp.org/tutorials))

  + **Deep Adversarial Learning for NLP** ([NAACL 2019](https://sites.cs.ucsb.edu/~william/papers/AdvNLP-NAACL2019.pdf))

- ### Information Retrieval

  + **Adversarial Machine Learning in Recommender Systems** ([ECIR 2021](https://www.youtube.com/watch?v=8V4TLdYMit8&list=PLted5MzCy6KwnlE3kFmeQJhDJCbS1lAt0&index=3), [RecSys 2020](https://www.youtube.com/watch?v=tjzykHbBd0w&list=PLted5MzCy6KwnlE3kFmeQJhDJCbS1lAt0&index=2), [WSDM 2020](https://github.com/sisinflab/amlrecsys-tutorial/blob/master/Tutorial-AML-RecSys-WSDM2020.pdf))

## Special Session

  + **Special Track on Safe and Robust AI** ([AAAI 2023](https://aaai.org/Conferences/AAAI-23/safeandrobustai/))

  + **Special Session on Adversarial Learning for Multimedia Understanding and Retrieval** ([ICMR 2022](https://al4mur.github.io/))

  + **Special Session on Adversarial Attack and Defense** ([APSIPA 2022](https://sites.google.com/ahduni.edu.in/2022-apsipa-ss-aad))

  + **Special Session on Information Security meets Adversarial Examples** ([WIFS 2019](https://signalprocessingsociety.org/WIFS2019/index9f1c.html?q=node/18))