PriDA2019: 1st International Workshop on Privacy-preserving Distributed Data Analysis Bogota, Colombia, June 5-7, 2019 |
Conference website | https://tinyurl.com/prida-html |
Submission link | https://easychair.org/conferences/?conf=prida2019 |
This workshop focuses on privacy-preserving and robust data analysis in the distributed setting. With the emerging technologies (e.g. IoT, FoG/EDGE computing, and 5G), the traditional cloud computing model, which often aggregates data and performs centralized analysis, faces new challenges, including greater cost in aggregating the exponentially-growing distributed data and stricter privacy regulations. Recently, we see an increasing demand for distributed data analysis (e.g. training new models in machine learning), that on one hand empowers the data owners to protect their data and on the other hand creates new business models.
The main focus of this workshop is to investigate privacy-preserving and robust solutions for distributed data analysis, and to empirically study their performances with respect to well-known datasets. The data analysis tasks of interest include various machine learning and data mining algorithms, but not necessarily limit to them. Some example technology topics we are looking for include the following:
- Lightweight secure multi-party computation techniques
- (Partial) homomorphic encryption techniques
- Functional encryption schemes
- Differential privacy: theory and implementations
- Syntactic data disclosure notions (k-anonymity, l-diversity, t-closeness, etc.)
- Robustness attack detection in the distributed setting
- Adversarial examples in machine learning
- Trade-offs between different requirements (privacy, robustness, utility, etc.)
- Properties other than privacy and robustness: transparency, anti-discrimination, bias
- Exploration of distributed ledger technologies (DLTs) and Blockchain
- Adaptation of data analysis algorithms to facilitate protecting privacy and robustness
Aiming at networking people from different communities and bridging the gap between them, we naturally welcome other related topics and contributed talks to this workshop. In the long-term, we wish the workshop could foster fruitful collaborations to investigate rigorous and usable solutions for the practitioners.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:
- Full papers describing privacy-preserving and robust solutions for distributed data analysis
- Posters describing research progress in privacy-preserving and robust solutions for distributed data analysis
Committees
Program Committee
- Erman Ayday, Case western reserve university, USA and Bilkent University, Turkey
- Gergely Biczok, Budapest University of Technology and Economics, Hungary
- Xiaofeng Chen, Xidian University, China
- Josep Domingo Ferrer, Universitat Rovira i Virgili, Spain
- Jinguang Han, University of Surrey, UK
- Jiuyong LI, University of South Australia, Australia
- Jianting Ning, NUS, Singapore
- Melek Onen, EURECOM, France
- Zhaohui Tang, SUTD, Singapore
- Jun Wang, University of Luxembourg, Luxembourg
- Jia Xu, Trustwave, Singapore
- Yang Zheng, SUTD, Singapore
- Jun Zhou, East China Normal University, China
Organizing committee
- Qiang Tang, Luxembourg Institute of Science and Technology, Luxembourg
Contact
All questions about submissions should be emailed to Qiang Tang (qiang.tang@list.lu)