EcoPaDL at ICML 2020

Workshop on ECOnomics of Privacy and Data Labor

About the workshop

Although data is considered to be the “new oil”, it is very hard to be priced. Raw use of data has been invaluable in several sectors such as advertising, healthcare, etc, but often in violation of people’s privacy. Labeled data has also been extremely valuable for the training of machine learning models (driverless car industry). This is also indicated by the growth of annotation companies such as Figure8 and Scale.AI, especially in the image space. Yet, it is not clear what is the right pricing for data workers who annotate the data or the individuals who contribute their personal data while using digital services. In the latter case, it is very unclear how the value of the services offered is compared to the private data exchanged. While the first data marketplaces have appeared, such as AWS,,, etc, they suffer from a lack of good pricing models. They also fail to maintain the right of the data owners to define how their own data will be used. There have been numerous suggestions for sharing data while maintaining privacy, such as training generative models that preserve original data statistics. While there are several proposals for solving the technical part of data markets and fair pricing, it is necessary to consider other aspects that are covered by researchers in the area of economics and law.


Time (EST) Title Presenter Slides Video
10:00am - 10:15am Designing Differentially Private Estimators in High Dimensions Aditya Dhar Slides Video
10:15am - 10:30am Really Useful Synthetic Data – A Framework to Evaluate the Quality of Differentially Private Synthetic Data Christian Arnold Slides Video
10:30am - 10:45am Generating Privacy-Preserving Synthetic Tabular Data Using Oblivious Variational Autoencoders L Vivek Harsha Slides Video
10:45am - 11:00am BREAK      
11:00am - 12:00pm Buying data over time Nicole Immorlica Slides Video
12:00pm - 12:15pm Optimal Query Complexity of Secure Stochastic Convex Optimization Wei Tang Slides Video
12:15pm - 12:30pm On Detecting Data Pollution Attacks On Recommender Systems Using Sequential GANs Behzad Shahrasbi Slides Video
12:30pm - 12:45pm Efficient Privacy-Preserving Stochastic Nonconvex Optimization Lingxiao Wang Slides Video
12:45pm - 1:30pm BREAK      
1:30pm - 1:45pm European Privacy Law and Global Markets for Data Christian Peukert Slides Video
1:45pm - 2:00pm To Call or not to Call? Using ML Prediction APIs more Accurately and Economically Lingjiao Chen Slides Video
2:00pm - 2:15pm Do Markets Make Sense for Personal Data? Aileen Nielsen Slides Video
2:15pm - 2:30:pm BREAK      
2:30pm - 3:30pm Intersectional Social Data Glen Weyl Slides Video

Call for papers

Important Dates

Paper submission deadline: June 5th 12th 2020, 11:59 PM (AoE, UTC-12)

Acceptance notification: July 1st 2020, EOD

Workshop: July 18th, 2020 (EST time zone)

Topics of interest

  • privacy-preserving machine learning methods
  • algorithmic economics
  • federated learning
  • data policies, AI ethics
  • pricing of machine learning models
  • data marketplaces
  • the economics of data labor and crowdsourcing
  • legal and ethical implications of data trading

Submission Guidelines

You are invited to submit papers of up to six pages. You have unlimitted space for references. If you you want to submit a longer paper, we ask that they write a 2-6 page summary and submit it, along with an attachment or link to the full paper. To be considered, papers must be received by the submission deadline (see Important Dates). Submissions must be original work and may be under submission to another venue at the time of review. Authors are encouraged to use the ICML 2020 style guidelines as described here, but they are free to use other formats..

Submission Site

Submission link:

All questions about submissions should be emailed to