Publications

While governments around the world are laying out plans to regulate AI, many businesses are woefully underprepared for the complexity of what lies ahead. With regulation about to roll through jurisdictions across the world, it is time to stop talking about what it might look like – and start acting.

Plans to create a data platform in partnership with the private sector could revolutionize NHS treatment, but concerns over data privacy and security are festering

New culture secretary decries ‘red tape,’ but detractors warn complexity could arise. The U.K. government plans to scrap the General Data Protection Regulation (GDPR), the EU’s data privacy regime it adopted before leaving the union.

The announcement this week by culture secretary Michelle Donelan that the UK plans to replace GDPR with its own “business and consumer-friendly British data protection system” is bad news. It will only increase complexity for companies in Britain and the EU hoping to collaborate by sharing data across geographical boundaries...

The UK currently holds a data adequacy agreement with the EU, enabling cross-border data flows. Any moves that threaten to disrupt that trade must be considered carefully and without ideology.

IT industry reacts to the [UK] government's plan to replace the pan-European data protection regulation

AI development is happening at breakneck speed, underpinned by massive investments from Big Tech companies such as Microsoft, Google and Amazon...But AI regulation need not be an obstruction to innovation.

AI regulation looks like the next continental differing of opinion — here's why the UK's approach could affect the rest of the Europe.

With the EU AI Act imminently being finalised and countries like the UK and the US recently publishing their national AI development strategies, the topic of regulation is being hotly debated.

Organizations are sitting on terabytes of data that only they can access. While it is highly valuable when used to inform key decisions for an individual company, the true power of data lies in collaboration between organizations.

ChatGPT has thrown artificial intelligence into the mainstream, and now it’s Rishi Sunak’s job to design rules which don’t prevent its growth, but keep us safe, writes Robin Röhm.

Microsoft’s recent purchase of a £1.5bn stake – equivalent to 4% – in the London Stock Exchange Group (LSEG) was the latest deal to unite Big Tech with a business running one of the world’s most significant financial markets.

Apheris is a platform for creating collaborative data ecosystems. The platform enables multiple organisations to extract value from each other's decentralised data sets, without changing the data’s location or sharing it.

Robin Röhn, CEO and co-founder and Berlin-based Apheris weighs in on what the US-EU artificial intelligence collaboration agreement means going forward, including what's sure to be a growing call for increased regulation.

AI for drug discovery has enormous potential; to unlock it, we need to reset our mindset. It feels like we have been talking about the world-changing possibilities of using AI in drug discovery for decades. Yes, machine learning could potentially transform the way new medicines are developed...

Advising the German Federal Office for Information Security on the Security of AI-Systems, Apheris provides an overview on attack vectors and threats of AI systems where external data is used or trained models are exposed to third parties. Recommendations are derived on how to systematically safeguard and test AI-systems.

We introduce PyVertical, a framework supporting vertical federated learning using split neural networks. The proposed framework allows a data scientist to train neural networks on data features vertically partitioned across multiple owners while keeping raw data on an owner's device.

The advancement of molecular profiling techniques fuels biomedical research with a deluge of data. To facilitate data sharing, the Global Alliance for Genomics and Health established the Beacon system, a search engine designed to help researchers find datasets of interest.

We present a multi-language, cross-platform, open-source library for asymmetric private set intersection (PSI) and PSI-Cardinality (PSI-C). Our protocol combines traditional DDH-based PSI and PSI-C protocols with compression based on Bloom filters that helps reduce communication in the asymmetric setting.