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Secure Multiparty Computation for Synthetic Data Generation from Distributed Data
Mayana Pereira · Sikha Pentyala · Martine De Cock · Anderson Nascimento · Rafael Timóteo de Sousa Júnior

Fri Dec 02 08:58 AM -- 09:00 AM (PST) @
in Synthetic Data for Empowering ML Research »
Event URL: https://openreview.net/forum?id=hpaVCT1Cx7p »

Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, finance, and education. Synthetic data generation algorithms with privacy guarantees are emerging as a paradigm to break this data logjam. Existing approaches, however, assume that the data holders supply their raw data to a trusted curator, who uses it as fuel for synthetic data generation. This severely limits the applicability, as much of the valuable data in the world is locked up in silos, controlled by entities who cannot show their data to each other or a central aggregator without raising privacy concerns.To overcome this roadblock, we propose the first solution in which data holders only share encrypted data for differentially private synthetic data generation. Data holders send shares to servers who perform Secure Multiparty Computation (MPC) computations while the original data stays encrypted. We instantiate this idea in an MPC protocol for the Multiplicative Weights with Exponential Mechanism (MWEM) algorithm to generate synthetic data based on real data originating from many data holders without reliance on a single point of failure.

Author Information

Mayana Pereira (Microsoft)
Sikha Pentyala (University of Washington, Tacoma)
Martine De Cock (University of Washington Tacoma)
Anderson Nascimento (University of Washington Tacoma)
Rafael Timóteo de Sousa Júnior (University of Brasilia)
Rafael Timóteo de Sousa Júnior

Rafael Timóteo de Sousa Júnior (https://orcid.org/0000-0003-1101-3029, WoS ResearcherID V-3293-2019) received his bachelor’s degree in electrical engineering, from the Federal University of Paraíba – UFPB, Campina Grande, Brazil, 1984, his master’s degree in computing and information systems, from the Ecole Supérieure d'Electricité – Supélec, Rennes, France, 1984-1985, and his doctorate in telecommunications and signal processing, from the University of Rennes 1, Rennes, France, 1988. He was a visiting researcher with the Group for Security of Information Systems and Networks (SSIR) at the Ecole Supérieure d'Electricité – Supélec, Rennes, France, 2006-2007. He worked in the private sector from 1988 to 1996. Since 1996, He is a Network Engineering Associate Professor in the Electrical Engineering Department, at the University of Brasília, Brazil, where he is the Coordinator of the Professional Post-Graduate Program on Electrical Engineering – Cybersecurity (PPEE) and supervises the Decision Technologies Laboratory (LATITUDE). IEEE Senior Member, he is Chair of the IEEE VTS Centro-Norte Brasil Chapter (IEEE VTS Chapter of the Year 2019) and the IEEE Centro-Norte Brasil Blockchain Group. He is a Researcher with the Productivity Fellowship Level 2 (PQ-2) granted by the Brazilian National Council for Scientific and Technological Development (CNPq). His professional experience includes research projects with Dell Computers, HP, IBM, Cisco, and Siemens. He has coordinated research, development, and technology transfer projects with the Brazilian Ministries of Planning, Economy, and Justice, as well as with the Institutional Security Office of the Presidency of Brazil, the Administrative Council for Economic Defense, the General Attorney of the Union and the Brazilian Union Public Defender. He has received research grants from the Brazilian research and innovation agencies CNPq, CAPES, FINEP, RNP, and FAPDF. He has developed research in cyber, information and network security, distributed data services and machine learning for intrusion and fraud detection, and signal processing, energy harvesting and security at the physical layer.

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