ookangzheng / apprenticeship

A notebook of awesome privacy protection,federated learning, fairness and blockchain research materials.

Apprenticeship

This part of my life is called "The Pursuit of Doctorate". Here is my blog about learning process.

Diving In

• Privacy protection

• Federated learning

• Fairness

• Blockchain

• Others

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Legend:

📒	📝	⌨️	📷	💾
PDF Files	Notes	Code	Slides	Other Supplementaries

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Awesome Privacy Protection Research Materials

A curated list of awesome privacy protection research materials.

• Books

Courses:

• Tutorial

Papers:

• Policy Brief
• Survey
• Basic Techniques
• Private Framework
• Private Benchmark
• Private Data Publishing
  • Transaction Data Publishing
  • Streaming Data Publishing
  • Graph Data Publishing
  • Image Data Publishing
• Private Data Analysis
  • Private Learning
  • Frequent Itemset Mining
  • Graph Data Analysis
  • Recommender Systems
  • Survival Analysis
  • Natural Language Processing
• Cryptography
  • Encrypted Database
  • Searchable Encryption

🔝

Books

Title	Authors	Published in	Year	Files	Notes	Supplementaries
数据库系统概论	王珊, 萨师煊	高等教育出版社	2014		📝

• | The Algorithmic Foundations of Differential Privacy | Cynthia Dwork, Aaron Roth | TCS | 2014 | [:ledger:](https://www.nowpublishers.com/article/Details/TCS-042) | | [:floppy_disk:](http://www.cis.upenn.edu/~aaroth/courses/privacyF11.html) |

• Jordi Soria-Comas, Josep Domingo-Ferrer: Optimal data-independent noise for differential privacy. Inf. Sci. 250: 200-214 (2013)

• Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu: Differential Privacy and Applications. Advances in Information Security 69, Springer 2017, ISBN 978-3-319-62002-2, pp. 1-222

• Ninghui Li, Min Lyu, Dong Su, Weining Yang: Differential Privacy: From Theory to Practice. Synthesis Lectures on Information Security, Privacy, & Trust, Morgan & Claypool Publishers 2016, pp. 1-138

🔝

Tutorial

	Instructors	Institution	Year	Files	Notes	Supplementaries
The U.S. Census Bureau Adopts Differential Privacy	John M. Abowd	U.S. Census Bureau	KDD 2018	📒		📷

• | The Algorithmic Foundations of Data Privacy | Aaron Roth | Penn | Fall 2011 | [:ledger:](http://www.cis.upenn.edu/~aaroth/courses/privacyF11.html) | | [:floppy_disk:](https://www.nowpublishers.com/article/Details/TCS-042) |

🔝

Policy Brief

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	China's Social Credit System: A Mark of Progress or a Threat to Privacy?	Martin Chorzempa, Paul Triolo, Samm Sacks		2018	📒	/	💾

🔝

Survey

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Differentially Private Data Publishing and Analysis: A Survey	Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu	TKDE	2017	📒
	Privacy for Recommender Systems: Tutorial Abstract	Bart P. Knijnenburg, Shlomo Berkovsky	RecSys	2017	📒		📷
	Privacy in Location-Based Services: State-of-the-Art and Research Directions	Mohamed F. Mokbel	MDM	2007	📒		📷

🔝

Basic Techniques

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
SVT-S	Understanding the Sparse Vector Technique for Differential Privacy	Min Lyu, Dong Su, Ninghui Li	VLDB	2017	📒
	Nearly-Optimal Private LASSO	Kunal Talwar, Abhradeep Thakurta, Li Zhang	NIPS	2015	📒	📝	📒
	Privacy-preserving statistical estimation with optimal convergence rates	Adam D. Smith	STOC	2011	📒
	Smooth sensitivity and sampling in private data analysis	Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith	STOC	2007	📒

🔝

Private Framework

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
KTELO	KTELO: A Framework for Defining Differentially-Private Computations	Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau	SIGMOD	2018	📒		⌨️

• PINQ: Frank McSherry: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. SIGMOD Conference 2009: 19-30; Davide Proserpio, Sharon Goldberg, Frank McSherry: Calibrating Data to Sensitivity in Private Data Analysis. PVLDB 7(8): 637-648 (2014)
• Fuzz: Marco Gaboardi, Andreas Haeberlen, Justin Hsu, Arjun Narayan, Benjamin C. Pierce: Linear dependent types for differential privacy. POPL 2013: 357-370
• PrivInfer: Gilles Barthe, Gian Pietro Farina, Marco Gaboardi, Emilio Jesús Gallego Arias, Andy Gordon, Justin Hsu, Pierre-Yves Strub: Differentially Private Bayesian Programming. ACM Conference on Computer and Communications Security 2016: 68-79
• LightDP: Danfeng Zhang, Daniel Kifer: LightDP: towards automating differential privacy proofs. POPL 2017: 888-901

🔝

Private Benchmark

• DPBench: Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang: Principled Evaluation of Differentially Private Algorithms using DPBench. SIGMOD Conference 2016: 139-154

🔝

Private Data Publishing

• | | Differentially private data publishing for data analysis | Dong Su | | 2016 | [:ledger:](https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=2220&context=open_access_dissertations) | | |
• | JTree | Differentially Private High-Dimensional Data Publication via Sampling-Based Inference | Rui Chen, Qian Xiao, Yu Zhang, Jianliang Xu | KDD | 2015 | [:ledger:](https://www.comp.hkbu.edu.hk/~xujl/Papers/kdd15.pdf) | | |
• | NoisyCut | Top-k frequent itemsets via differentially private FP-trees | Jaewoo Lee, Christopher W. Clifton | KDD | 2014 | [:ledger:](https://cybersecurity.uga.edu/publications/VI_KDD2014.pdf) | | |
• | PTT<br>k-RecursiveMedians | Differentially Private Algorithms for Empirical Machine Learning | Ben Stoddard, Yan Chen, Ashwin Machanavajjhala | CoRR | 2014 | [:ledger:](https://arxiv.org/pdf/1411.5428.pdf) | | |

Transaction Data Publishing

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
PATE-GAN	PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees	James Jordon, Jinsung Yoon, Mihaela van der Schaar	ICLR	2019	📒
Liancheng	Privacy as a Service: Publishing Data and Models	Ashish Dandekar, Debabrota Basu, Thomas Kister, Geong Sen Poh, Jia Xu, Stéphane Bressan	DASFAA	2019	📒
	A Data Publishing System Based on Privacy Preservation	Zhihui Wang, Yun Zhu, Xuchen Zhou	DASFAA	2019	📒
	Approximate Query Processing using Deep Generative Models	Saravanan Thirumuruganathan, Shohedul Hasan, Nick Koudas, Gautam Das	CoRR	2019	📒
G-PATE	Scalable Differentially Private Generative Student Model via PATE	Yunhui Long, Suxin Lin, Zhuolin Yang, Carl A. Gunter, Bo Li	CoRR	2019	📒
DP-GAN-DNN	POSTER: A Unified Framework of Differentially Private Synthetic Data Release with Generative Adversarial Network	Pei-Hsuan Lu, Chia-Mu Yu	CCS	2017	📒		⌨️
PrivBayes	PrivBayes: Private Data Release via Bayesian Networks	Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, Xiaokui Xiao	TODS SIGMOD	2017 2014	📒 📒		⌨️ 📷📷
	Efficient privacy-preserving temporal and spacial data aggregation for smart grid communications	Xiaolei Dong, Jun Zhou, Zhenfu Cao	Concurrency	2016	📒	📝

🔝

Streaming Data Publishing

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
PeGaSus	PeGaSus: Data-Adaptive Differentially Private Stream Processing	Yan Chen, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau	CCS	2017	📒	📝

• | CCDPSD | 异方差加噪下差分隐私流数据发布一致性优化算法 | 孙岚, 康健, 吴英杰, 张立群 | 清华大学学报 | 2018 | [:ledger:](http://kns.cnki.net/KCMS/detail/11.2223.N.20180921.0900.001.html) | | |
• | | 面向实时数据流的差分隐私直方图发布技术 | 杨庚, 夏春婷, 白云璐 | 南京邮电大学学报 | 2018 | [:ledger:](http://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CJFDLAST2018&filename=NJYD201802014) | | |
• | PrivTree | PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions | Jun Zhang, Xiaokui Xiao, Xing Xie | SIGMOD | 2016 | [:ledger:](http://delivery.acm.org/10.1145/2890000/2882928/p155-zhang.pdf) | | |

🔝

Graph Data Publishing

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
GSN-DP	Geo-social network publication based on differential privacy	Xiaochun Wang, Yidong Li	FCS	2018	📒	📝	📷

🔝

Image Data Publishing

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
RPM	Random permutation Maxout transform for cancellable facial template protection	Andrew Beng Jin Teoh, Sejung Cho, Jihyeon Kim	MTA	2018	📒	📝
BEMK	面向人脸图像发布的差分隐私保护	张啸剑, 付聪聪, 孟小峰	JIG	2018	📒	📝
DPGAN	Differentially Private Generative Adversarial Network	Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, Jiayu Zhou	CoRR	2018	📒		⌨️

🔝

Private Data Analysis

Private Learning

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Robust anomaly detection and backdoor attack detection via differential privacy	Min Du, Ruoxi Jia, Dawn Song	ICLR	2020	📒		📝⌨️
	Differentially Private Meta-Learning	Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar	ICLR	2020	📒	📝
	Privacy Enhanced Multimodal Neural Representations for Emotion Recognition	Mimansa Jaiswal, Emily Mower Provost	AAAI	2020	📒
	Understanding Gradient Clipping in Private SGD: A Geometric Perspective	Xiangyi Chen, Zhiwei Steven Wu, Mingyi Hong	CoRR	2020	📒
	Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy	Kareem Amin, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii	ICML	2019	📒		📝📷📷
	A General Approach to Adding Differential Privacy to Iterative Training Procedures	H. Brendan McMahan, Galen Andrew	CoRR	2018	📒		⌨️
AdaClip	AdaCliP: Adaptive Clipping for Private SGD	Venkatadheeraj Pichapati, Ananda Theertha Suresh, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar	CoRR	2019	📒
	Differentially Private Learning with Adaptive Clipping	Om Thakkar, Galen Andrew, H. Brendan McMahan	CoRR	2019	📒
DP-FedAvg	Learning Differentially Private Recurrent Language Models	H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang	ICLR	2018	📒	📝
	Three Tools for Practical Differential Privacy	Koen Lennart van der Veen, Ruben Seggers, Peter Bloem, Giorgio Patrini	PPML	2018	📒
dp-GAN	Differentially Private Releasing via Deep Generative Model	Xinyang Zhang, Shouling Ji, Ting Wang	CoRR	2018	📒		⌨️
	Differentially Private Federated Learning: A Client Level Perspective	Robin C. Geyer, Tassilo Klein, Moin Nabi	CoRR	2017	📒		⌨️ 💾
DPSGD	Deep Learning with Differential Privacy	Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang	CCS	2016	📒		💾 ⌨️

🔝

Frequent Itemset Mining

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
Diff-FPM	Mining frequent graph patterns with differential privacy	Entong Shen, Ting Yu	KDD	2013	📒		📷

• | PrivBasis | PrivBasis: Frequent Itemset Mining with Differential Privacy | Ninghui Li, Wahbeh H. Qardaji, Dong Su, Jianneng Cao | PVLDB | 2012 | [:ledger:](https://dl.acm.org/citation.cfm?id=2350251) | | [:keyboard:](https://github.com/DongSuIBM/PrivBasis) |

🔝

Graph Data Analysis

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
LPGNN	Locally Private Graph Neural Networks	Sina Sajadmanesh, Daniel Gatica-Perez	CoRR	2020	📒		⌨️

🔝

Recommender Systems

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
FedMF	Secure Federated Matrix Factorization	Di Chai, Leye Wang, Kai Chen, Qiang Yang	FML	2019	📒	📝	⌨️
GD-DR	Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy	Hyejin Shin, Sungwook Kim, Junbum Shin, Xiaokui Xiao	TKDE	2018	📒	📝
PrivSR	Personalized Privacy-Preserving Social Recommendation	Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, Yujun Zhang	AAAI	2018	📒	📝	💾
DMF	Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization	Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, Xiaolong Li	AAAI	2018	📒	📝
EpicRec	EpicRec: Towards Practical Differentially Private Framework for Personalized Recommendation	Yilin Shen, Hongxia Jin	CCS	2016	📒
DPMF	Differentially Private Matrix Factorization	Jingyu Hua, Chang Xia, Sheng Zhong	IJCAI	2015	📒	📝
DP-UnP3R	Privacy-Preserving Personalized Recommendation: An Instance-Based Approach via Differential Privacy	Yilin Shen, Hongxia Jin	ICDM	2014	📒

🔝

Survival Analysis

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Differentially Private Survival Function Estimation	Lovedeep Gondara, Ke Wang	CoRR	2019	📒		⌨️

🔝

Natural Language Processing

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
DPNR	Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness	Lingjuan Lyu, Xuanli He, Yitong Li	EMNLP	2020	📒		⌨️

🔝

Cryptography

Encrypted Database

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
CryptZip	Sa Wang, Yiwen Shao, Yungang Bao	Practices of backuping homomorphically encrypted databases	FCS	2019	📒		📷

🔝

Searchable Encryption

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
SPiRiT	Secure Search on Encrypted Data via Multi-Ring Sketch	Adi Akavia, Dan Feldman, Hayim Shaul	CCS	2018	📒	📝
CPABKS	Secure and Efficient Attribute-Based Encryption with Keyword Search	Haijiang Wang, Xiaolei Dong, Zhenfu Cao, Dongmei Li	CJ	2018	📒	📝

🔝

Awesome Federated Learning Research Materials

A curated list of awesome federated learning research materials.

Courses:

• Tutorial

Papers:

• Survey
• Algorithm
  • Communication
  • Aggregation
  • Application
• System

🔝

Tutorial

	Instructors	Institution	Year	Files	Notes	Supplementaries
GDPR, Data Shortage and AI	Qiang Yang	HKUST	2019			📷

🔝

Survey

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Federated Machine Learning: Concept and Applications	Qiang Yang, Yang Liu, Tianjian Chen, Yongxin Tong	TIST	2019	📒	📝	💾
	Federated Learning	Florian Hartmann		2018	📒		⌨️⌨️⌨️

🔝

Algorithm

Communication

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Efficient and Robust Asynchronous Federated Learning with Stragglers	Ming Chen, Bingcheng Mao, Tianyi Ma	CoRR	2020	📒

🔝

Aggregation

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
q-FedAvg	Fair Resource Allocation in Federated Learning	Tian Li, Maziar Sanjabi, Virginia Smith	ICLR	2020	📒
AFL	Agnostic Federated Learning	Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh	ICML	2019	📒		💾 📷
FedAvg	Communication-Efficient Learning of Deep Networks from Decentralized Data	Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas	AISTATS	2017	📒		💾

🔝

Application

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
Federated CIFG	Federated Learning for Mobile Keyboard Prediction	Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, Daniel Ramage	CoRR	2018	📒		💾
	Federated Meta-Learning for Recommendation	Fei Chen, Zhenhua Dong, Zhenguo Li, Xiuqiang He	CoRR	2018	📒

🔝

System

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Towards Federated Learning at Scale: System Design	Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander	SysML	2019	📒

🔝

Awesome Fairness Research Materials

A curated list of awesome fairness research materials.

Courses:

• Tutorial
• Causal Fairness

Papers:

• Survey
• Statistical Fairness
• Causal Fairness
• Resource Allocation
• Others
  • Stability

🔝

Tutorial

	Instructors	Institution	Year	Files	Notes	Supplementaries
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned	Sarah Bird, Ben Hutchinson, Krishnaram Kenthapadi, Emre Kıcıman, Margaret Mitchell	Microsoft Google LinkedIn	KDD 2019		📝	📷
Challenges of incorporating algorithmic fairness into industry practice	Henriette Cramer, Ken Holstein, Jenn Wortman Vaughan, Hal Daumé III, Miroslav Dudík, Hanna Wallach, Sravana Reddy, Jean Garcia-Gathright	Microsoft Research	ACM FAccT 2019			📷

🔝

Causal Fairness

	Instructors	Institution	Year	Files	Notes	Supplementaries
Counterfactual reasoning in algorithmic fairness	Ricardo Silva	UCL Alan Turing Institute	FairWare @ ICSE 2018			📷
Fairness in Machine Learning and Its Causal Aspects	Ricardo Silva	UCL Alan Turing Institute	2017			📷

🔝

Survey

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness	Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab K. Patro, Manish Raghavan, Ana-Andreea Stoica, Stratis Tsirtsis	CoRR	2020	📒
	A Survey on Bias and Fairness in Machine Learning	Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan	CoRR	2019	📒
	The Frontiers of Fairness in Machine Learning	Alexandra Chouldechova, Aaron Roth	CoRR	2018	📒

🔝

Statistical Fairness

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
CFair	Conditional Learning of Fair Representations	Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon	ICLR	2020	📒	📝	📝
Fairness warnings fair-MAML	Fairness warnings and fair-MAML: learning fairly with minimal data	Dylan Slack, Sorelle A. Friedler, Emile Givental	FAT*	2020	📒		📝📷📷⌨️
AdvDebias	Inherent Tradeoffs in Learning Fair Representations	Han Zhao, Geoffrey J. Gordon	NeurIPS	2019	📒	📝	📝📝📝📷📷⌨️
Random Repair	Obtaining Fairness using Optimal Transport Theory	Paula Gordaliza, Eustasio del Barrio, Fabrice Gamboa, Jean-Michel Loubes	ICML	2019	📒		📒⌨️📷
FFVAE	Flexibly Fair Representation Learning by Disentanglement	Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel	ICML	2019	📒		📒📷
DP-postprocessing DP-oracle-learner	Differentially Private Fair Learning	Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman	ICML	2019	📒		📒📷
Fair Regression	Fair Regression: Quantitative Definitions and Reduction-Based Algorithms	Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu	ICML	2019	📒		📒⌨️⌨️📷
Pairwise Fairness	Fairness in Recommendation Ranking through Pairwise Comparisons	Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow	KDD	2019	📒	📝	📷
Strong Demographic Parity	Wasserstein Fair Classification	Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa	UAI	2019	📒		📒⌨️
	Exploring Human Gender Stereotypes with Word Association Test	Yupei Du, Yuanbin Wu, Man Lan	EMNLP	2019	📒		⌨️
AVD Penalizers SD Penalizers	Penalizing Unfairness in Binary Classification	Yahav Bechavod, Katrina Ligett	CoRR	2017	📒		⌨️
Equalized Odds	Equality of Opportunity in Supervised Learning	Moritz Hardt, Eric Price, Nati Srebro	NIPS	2016	📒		📒📝

🔝

Causal Fairness

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
Counterfactual Privilege	Making Decisions that Reduce Discriminatory Impacts	Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva	ICML	2019	📒		📒⌨️ 📷📷
Fair K Fair Add	Counterfactual Fairness	Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva	NIPS	2017	📒	📝	📒⌨️📷📷
Multi-World Fairness	When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness	Chris Russell, Matt J. Kusner, Joshua R. Loftus, Ricardo Silva	NIPS	2017	📒	📝	📷

🔝

Resource Allocation

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
FairCo	Controlling Fairness and Bias in Dynamic Learning-to-Rank	Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims	SIGIR	2020	📒		📷⌨️
	Fair decision making using privacy-protected data	David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau	FAT*	2020	📒		📷📷
DPSGD-F	Removing Disparate Impact of Differentially Private Stochastic Gradient Descent on Model Accuracy	Depeng Xu, Wei Du, Xintao Wu	CoRR	2020	📒
FEN	Learning Fairness in Multi-Agent Systems	Jiechuan Jiang, Zongqing Lu	NeurIPS	2019	📒		📒📝📝📝⌨️
	Differential Privacy Has Disparate Impact on Model Accuracy	Eugene Bagdasaryan, Omid Poursaeed, Vitaly Shmatikov	NeurIPS	2019	📒		📝📝📝📷⌨️
DC Maximin	Group-Fairness in Influence Maximization	Alan Tsang, Bryan Wilder, Eric Rice, Milind Tambe, Yair Zick	IJCAI	2019	📒		📝⌨️
TREE01	Fairness without Harm: Decoupled Classifiers with Preference Guarantees	Berk Ustun, Yang Liu, David C. Parkes	ICML	2019	📒		📒⌨️📷
Greedy Capture Local Capture	Proportionally Fair Clustering	Xingyu Chen, Brandon Fain, Liang Lyu, Kamesh Munagala	ICML	2019	📒		📝⌨️📷
GF1A/B	Group Fairness for the Allocation of Indivisible Goods	Vincent Conitzer, Rupert Freeman, Nisarg Shah, Jennifer Wortman Vaughan	AAAI	2019	📒		📝
FULTR	Fair Learning-to-Rank from Implicit Feedback	Himank Yadav, Zhengxiao Du, Thorsten Joachims	CoRR	2019	📒

🔝

Others

Stability

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
Stable-Fair	Stable and Fair Classification	Lingxiao Huang, Nisheeth K. Vishnoi	ICML	2019	📒		📝

🔝

Awesome Blockchain Research Materials

A curated list of awesome blockchain research materials.

• Survey
• Network Layer

🔝

Survey

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
Yellow Paper	Ethereum: A Secure Decentralised Generalised Transaction Ledger	Dr. Gavin Wood			📒
黄皮书	以太坊：一种安全去中心化的通用交易账本	崔广斌, 高天露			📒
	Untangling Blockchain: A Data Processing View of Blockchain Systems	Tien Tuan Anh Dinh, Rui Liu, Meihui Zhang, Gang Chen, Beng Chin Ooi, Ji Wang	TKDE	2018	📒		💾
	区块链隐私保护研究综述	祝烈煌, 高峰, 沈蒙, 李艳东, 郑宝昆, 毛洪亮, 吴震	计算机研究与发展	2017	📒

🔝

Network Layer

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
BlockFL	On-Device Federated Learning via Blockchain and its Latency Analysis	Hyesung Kim, Jihong Park, Mehdi Bennis, Seong-Lyun Kim	CoRR	2018	📒

🔝

Other Awesome Research Materials

A curated list of awesome research materials.

• Artificial Intelligence
• Large-Scale Optimization
• Combinatorial optimization

🔝

Artificial Intelligence

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
	Meta-Learning in Neural Networks: A Survey	Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, Amos J. Storkey	CoRR	2020	📒	📝
	AI Governance in 2019 a Year in Review	Qian Shi, Hui Li, Brian Tse, John Hopcroft, Stuart Russell, Caroline Jeanmaire, Qiang Yang, Pascale Fung, Roman Yampolskiy, Allan Dafoe, Markus Anderljung, Gillian K. Hadfield, Jun Su, Thilo Hagendorff, Petra Ahrweiler, Robin Williams, Colin Allen, Poon King Wang, Ferran Jarabo Carbonell, Xiaohong Wang, Qingfeng Yang, Qi Yin, Don Wright, Miles Brundage, Jack Clark, Irene Solaiman, Gretchen Krueger, Seán Ó hÉigeartaigh, Helen Toner, Millie Liu, Steve Hoffman, Irakli Beridze, Wendell Wallach, Cyrus Hodes, Nicolas Miailhe, Jessica Cussins Newman, Dingding Chen, Eva Kaili, Francesca Rossi, Charlotte Stix, Angela Daly, Danit Gal, Arisa Ema, Goh Yihan, Nydia Remolina, Urvashi Aneja, Ying Fu, Zhiyun Zhao, Xiuquan Li, Weiwen Duan, Qun Luan, Rui Guo, Yingchun Wang	Shanghai Institute for Science of Science	2020	📒	📝
Meta-Weight-Net	Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting	Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, Deyu Meng	NeurIPS	2019	📒	📝📝📝	📒⌨️
	Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI	Alejandro Barredo Arrieta, Natalia Díaz Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, Francisco Herrera	CoRR	2019	📒	📝
	Efficient Per-Example Gradient Computations	Ian J. Goodfellow	CoRR	2015	📒

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Large-Scale Optimization

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
BDA	A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton	Risheng Liu, Pan Mu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang	ICML	2020	📒
AGD+	On Acceleration with Noise-Corrupted Gradients	Michael Cohen, Jelena Diakonikolas, Lorenzo Orecchia	ICML	2018	📒		💾

• Olivier Devolder, François Glineur, Yurii Nesterov: First-order methods of smooth convex optimization with inexact oracle. Math. Program. 146(1-2): 37-75 (2014)
• Alexandre d'Aspremont: Smooth Optimization with Approximate Gradient. SIAM Journal on Optimization 19(3): 1171-1183 (2008)

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Combinatorial optimization

	Title	Authors	Published in	Year	Files	Notes	Supplementaries
GSLS	Optimizing top-k retrieval: submodularity analysis and search strategies	Chaofeng Sha, Keqiang Wang, Dell Zhang, Xiaoling Wang, Aoying Zhou	FCS WAIM	2016 2014	📒 📒
SubmEP	Ensemble Pruning: A Submodular Function Maximization Perspective	Chaofeng Sha, Keqiang Wang, Xiaoling Wang, Aoying Zhou	DASFAA	2014	📒

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