Differentially Private Block Coordinate Descent for Linear Regression on Vertically Partitioned Data

We present a differentially private extension of the block coordinate descent algorithm by means of objective perturbation. The algorithm iteratively performs linear regression in a federated setting on vertically partitioned data. In addition to a privacy guarantee, we derive a utility guarantee; a tolerance parameter indicates how much the...

Federated tool for anonymization and annotation in image data

The increasing complexity of security challenges requires Law Enforcement Agencies (LEAs) to have improved analysis capabilities, e.g., with the use of Artificial Intelligence (AI). However, it is challenging to make large enough high-quality training and testing datasets available to the community that is developing AI tools to support LEAs in...

Secure Sparse Gradient Aggregation in Distributed Architectures

Federated Learning allows multiple parties to train a model collaboratively while keeping data locally. Two main concerns when using Federated Learning are communication costs and privacy. A technique proposed to significantly reduce communication costs and increase privacy is Partial Weight Sharing (PWS). However, this method is insecure due to...

Technologische doorbraak eindelijk een privacyvriendelijke manier om data te benutten [Technological breakthrough finally, a privacy-friendly way to harness data]

Data sharing and analysis are essential when it comes to achieving economic growth and solving societal challenges. However, data sharing is yet to really get off the ground due to commercial and/or legal barriers, including the fundamental right to privacy. Innovative technologies such as Federated Learning and Multi-Party Computation offer a...