This research track concentrates on utilizing privacy-preserving computation to tackle challenges associated with Maximally Extractable Value (MEV). The focus is on the potential of Fully Homomorphic Encryption (FHE) based approaches and the accelerated advancements in the field due to software and hardware improvements. The aim is to contribute to privacy-preserving solutions that are not only theoretically sound but also practically appealing to researchers and retail users, unlocking new use cases beyond merely offering privacy guarantees.
Projects in this track will also explore the benefits of other Privacy Enhancing Technologies (PETs) such as Differential Privacy (DP), and aim to balance the privacy/efficiency trade-off that remains pivotal to these methods.
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