Building robust ways to outwit MEV strategies by network validators in cryptocurrency applications is garnering rapid interests now. Encrypting mempools with (varieties of) threshold encryption systems is seen as one of the leading solutions to this problem. Particularly, the notion of Batched Threshold Encryption (BTE) proposed in multiple recent papers by Choudhuri et al. [USENIX 2024, 2025] and Agarwal et al. [CRYPTO 2025] gives promising approaches to build scalable encrypted mempools. However, the existing BTE does not address collusion among (potentially malicious) validators (leaking confidential information).

The rising commerciality of cryptocurrencies and blockchains in DeFi applications raises the importance of implementing robust methods to protect its regular users against parties with enormous amounts of resources that allows them to balefully influence the market through various Maximal Extractable Value (MEV) strategies. There are typical situations in such scenarios where an end-user may want to hide its transaction details till it has been executed. Ensuring the privacy of pending mempool transactions thus becomes an important goal. To this end, various decentralized versions of threshold encryption (TE) systems have been recently shown particularly effective.

Proof of Storage-Time (PoST) is a cryptographic primitive that enables a server to demonstrate non-interactive continuous availability of outsourced data in a publicly verifiable way.

Generative models of protein sequence families are an important tool in the repertoire of protein scientists and engineers alike. However, state-of-the-art generative approaches face inference, accuracy, and overfitting- related obstacles when modeling moderately sized to large proteins and/or protein families with low sequence coverage. Here, we present a simple to learn, tunable, and accurate generative model, GENERALIST: GENERAtive nonLInear tenSor-factorizaTion for protein sequences. GENERALIST accurately captures several high order summary statistics of amino acid covariation. GENERALIST also predicts conservative local optimal sequences which are likely to fold in stable 3D structure. Importantly, unlike current methods, the density of sequences in GENERALIST-modeled sequence ensembles closely resembles the corresponding natural ensembles. Finally, GENERALIST embeds protein sequences in an informative latent space. GENERALIST will be an important tool to study protein sequence variability.

Overcoming the threat of coercion attacks in a cryptographic system has been a top priority for system designers since the birth of cyber-security. One way to overcome such a threat is to leverage implicit memory to construct a defense against rubber-hose attacks where the users themselves do not possess conscious knowledge of the trained password. We propose NeuroCrypt, a coercion-resistant authentication system that uses an improved version of the Serial Interception Sequence Learning task, employing additional auditory and haptic modalities backed by concepts borrowed from cognitive psychology. We carefully modify the visual stimuli as well as add auditory and haptic stimuli to improve the implicit learning process, resulting in faster training and longer retention. Moreover, our improvements guarantee that explicit recognition of the trained passwords remains suppressed.

Federated learning allows us to distributively train a machine learning model where multiple parties share local model parameters without sharing private data. However, parameter exchange may still leak information. Several approaches have been proposed to overcome this, based on multi-party computation, fully homomorphic encryption, etc.; many of these protocols are slow and impractical for real-world use as they involve a large number of cryptographic operations. In this paper, we propose the use of Trusted Execution Environments (TEE), which provide a platform for isolated execution of code and handling of data, for this purpose. We describe Flatee, an efficient privacy-preserving federated learning framework across TEEs, which considerably reduces training and communication time. Our framework can handle malicious parties (we do not natively solve adversarial data poisoning, though we describe a preliminary approach to handle this).