A Demonstration of Sterling: A Privacy-Preserving Data Marketplace

【Author】 Hynes, Nick; Dao, David; Yan, David; Cheng, Raymond; Song, Dawn

【Source】PROCEEDINGS OF THE VLDB ENDOWMENT

【影响因子】3.557

【Abstract】In this work, we demonstrate Sterling, a decentralized marketplace for private data. Sterling enables privacy-preserving distribution and use of data by using privacy-preserving smart contracts which run on a permissionless blockchain. The privacy-preserving smart contracts, written by data providers and consumers, immutably and irrevocably represent the interests of their creators. In particular, we provide a mechanism for data providers to control the use of their data through automatic verification of data consumer contracts, allowing providers to express constraints such as pricing and differential privacy. Through smart contracts and trusted execution environments, Sterling enables privacy-preserving analytics and machine learning over private data in an efficient manner. The resulting economy ensures that the interests of all parties are aligned. For the demonstration, we highlight the use of Sterling for training machine learning models on individuals' health data. In doing so, we showcase novel approaches to automatically appraising training data, verifying and enforcing model privacy properties, and efficiently training private models on the blockchain using trusted hardware.

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【发表时间】2018 AUG

【收录时间】2022-01-02

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