Lomas: A Platform for Confidential Analysis of Private Data
Damien Aymon, Dan-Thuy Lam, Lancelot Marti, Pauline Maury-Laribi\`ere,, Christine Choirat, Rapha\"el de Fondeville

TL;DR
Lomas is an open-source platform enabling secure, privacy-preserving analysis of confidential public datasets through trusted computing and differential privacy, unlocking valuable insights while ensuring data confidentiality.
Contribution
It introduces a novel platform that allows authorized analysis of private data without direct access, using trusted environments and differential privacy for enhanced security.
Findings
Enables analysis without data exposure
Uses differential privacy to protect individual information
Facilitates secure, compliant data analysis for public services
Abstract
Public services collect massive volumes of data to fulfill their missions. These data fuel the generation of regional, national, and international statistics across various sectors. However, their immense potential remains largely untapped due to strict and legitimate privacy regulations. In this context, Lomas is a novel open-source platform designed to realize the full potential of the data held by public administrations. It enables authorized users, such as approved researchers and government analysts, to execute algorithms on confidential datasets without directly accessing the data. The Lomas platform is designed to operate within a trusted computing environment, such as governmental IT infrastructure. Authorized users access the platform remotely to submit their algorithms for execution on private datasets. Lomas executes these algorithms without revealing the data to the user and…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Privacy, Security, and Data Protection · Digital and Cyber Forensics
