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Privacy-preserving Services On The Internet

Sector: Broadband • Location: Germany

Source: EU Funding & Tenders Portal

Project
Ended

Today, when using services on the Internet, users have to fully entrust a single service provider with their data. Many of these service providers are located outside the EU and there are cases where data has not only been leaked by attacks of outsiders or insiders, but also by governments who built backdoors into software or hardware, or forced service providers to give out sensitive user data. W

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The project “Privacy-preserving Services On The Internet” is an infrastructure initiative in the Broadband sector, located in Germany. Taiyo aggregates data on it from EU Funding & Tenders Portal.

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Description

Description

Today, when using services on the Internet, users have to fully entrust a single service provider with their data. Many of these service providers are located outside the EU and there are cases where data has not only been leaked by attacks of outsiders or insiders, but also by governments who built backdoors into software or hardware, or forced service providers to give out sensitive user data. With the new EU General Data Protection Regulation (GDPR) also companies have an obligation to properly protect users’ data. My project PSOTI will eliminate the need to trust a single service provider and empower users to freely control their data. For this, the users can choose a subset of multiple service providers that they are willing to trust who jointly process their data and privacy is guaranteed even if all but one are compromised. The main goal of PSOTI is to develop privacy-preserving services for commonly used tasks on the Internet that are feature-rich and efficient enough for practical use. This will allow to privately store, retrieve, search, and process data, and help to comply with the GDPR and preserve the fundamental rights to privacy and the protection of personal data. As underlying technology, we will build a real-world secure multi-party computation (MPC) framework that can also be used for other large-scale privacy-preserving applications such as genomics or machine learning. To achieve our main goal, we will solve the following challenges: 1) Develop private query protocols on outsourced data that process complex queries such as Boolean formulas over string matches or range queries, and even hide the query’s structure. 2) Build a real-world MPC framework that scales to large functionalities, is highly parallelized, interoperable, and fully integrated. 3) Demonstrate real-world applicability for privacy-preserving and feature-rich services on the Internet such as file storage (going beyond Dropbox), surveys (going beyond Google Forms), and email.

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High

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100%

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