Privacy-Preserving Analytics, Processing and Data Management
| Autor: | Dan Bogdanov, Per Gunnar Auran, Kalmer Keerup, Baldur Kubo |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
021110 strategic
defence & security studies Guard (information security) Computer science business.industry Data management 0211 other engineering and technologies Homomorphic encryption 02 engineering and technology Computer security computer.software_genre Competitive advantage Software deployment Analytics 020204 information systems 0202 electrical engineering electronic engineering information engineering Secure multi-party computation Confidentiality 14. Life underwater business computer |
| Zdroj: | Big Data in Bioeconomy ISBN: 9783030710682 |
| Popis: | Typically, data cannot be shared among competing organizations due to confidentiality or regulatory restrictions. We present several technological alternatives to solve the problem: secure multi-party computation (MPC), trusted execution environments (TEE) and multi-key fully homomorphic encryption (MKFHE). We compare these privacy-enhancing technologies from deployment and performance point of view and explain how we selected technology and machine learning methods. We introduce a demonstrator built in the DataBio project for securely combining private and public data for planning of fisheries. The secure machine learning of best catch locations is a web solution utilizing Intel® Software Guard Extensions (Intel® SGX)-based TEE and built with the Sharemind HI (Hardware Isolation) development tools. Knowing where to go fishing is a competitive advantage that a fishery is not interested to share with competitors. Therefore, joint intelligence from public and private sector data while protecting secrets of each contributing organization is an important enabler. Finally, we discuss the wider business impact of secure machine learning in situations where data confidentiality is a concern. |
| Databáze: | OpenAIRE |
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