International Association
for Cryptologic Research

We are proud to announce the winners of the 2025 IACR Test-of-Time Award for Asiacrypt.

The IACR Test-of-Time Award honors papers published at the 3 IACR flagship conferences 15 years ago which have had a lasting impact on the field.

The Test-of-Time award for Asiacrypt 2010 is awarded to the following paper:

Constant-Size Commitments to Polynomials and Their Applications
by Aniket Kate, Gregory M. Zaverucha, and Ian Goldberg

For introducing the first constant-size polynomial commitment scheme, a cornerstone of modern succinct zero-knowledge proofs.

Congratulations to the winners!

In this work, we initiate the formal study of oblivious batch updates over outsourced encrypted Bloom filters, focusing on scenarios where a storage-limited sender must insert or delete batches of elements in a Bloom filter maintained on an untrusted server. Our survey identifies only two prior approaches (CCS 2008 and CCS 2012) that can be adapted to this problem. However, they either fail to provide adequate security in dynamic scenarios or incur prohibitive update costs that scale with the filter’s maximum capacity rather than the actual batch size.

To address these limitations, we introduce a new cryptographic primitive, $\textit{Oblivious Bloom Filter Insertion}$ ($\textsf{OBFI}$), and propose novel constructions. At the core of our design is a novel building block, $\textit{Oblivious Bucket Distribution}$ ($\textsf{OBD}$), which enables a storage-limited sender to distribute a large array of elements, uniformly sampled from a finite domain, into small, fixed-size buckets in a data-oblivious manner determined by element order. The design of $\textsf{OBD}$ is further supported by identifying and proving a new structural property of such arrays, which establishes tight and explicit probabilistic bounds on the number of elements falling within predefined subranges of the domain.

Our $\textsf{OBFI}$ constructions achieve adaptive data-obliviousness and ensure that batch update costs scale primarily with the batch size. Depending on the variant, the sender’s storage requirement ranges from $O(\lambda)$, where $\lambda$ is the security parameter, down to $O(1)$. Finally, we demonstrate the practicality of $\textsf{OBFI}$ by integrating it into representative Bloom-filter-based cryptographic protocols for Searchable Symmetric Encryption, Public-key Encryption with Keyword Search, and Outsourced Private Set Intersection, thereby obtaining batch-updatable counterparts with state-of-the-art security and performance.

A Batched Threshold Encryption (BTE) scheme enables a committee of servers to perform a lightweight (in terms of communication and computation) threshold decryption of an arbitrary batch of ciphertexts from a larger pool, while ensuring the privacy of ciphertexts that are outside the batch. Such a primitive has a direct application in designing encrypted mempools for MEV protection in modern blockchains. Bormet et al. (USENIX 2025) recently proposed a BTE scheme called “BEAT-MEV” which is concretely efficient for small to moderate batch sizes.

In this work, we improve and extend the BEAT-MEV scheme in multiple ways. First, we improve the computational cost from quadratic to quasilinear in the batch size, thus making it practical for large batch sizes. This improvement is achieved by substituting the key-homomorphic punctured PRF used in BEAT-MEV with an FFT-friendly alternative. Second, we extend the ideas in their scheme to the weighted setting, where each server in the committee has an associated 'weight' value (e.g., stake weight of validators in PoS blockchains), while crucially ensuring that the communication cost remains independent of the weights. In contrast, BEAT-MEV with naive virtualization would incur communication cost linear in the total weight. Third, for handling the small failure rate inherent in BEAT-MEV scheme due to index collisions across different clients at the time of encryption, we propose a generalization of their suggested approach which offers an option to trade off between ciphertext size and server communication for a given failure rate.

We implement and evaluate our scheme and compare it with BEAT-MEV to demonstrate our concrete improvement. In the unweighted setting, we improve the computational cost (without increasing the communication cost) by ≈ 6× for a batch size of 512 ciphertexts. In the weighted setting, we improve the communication cost (without compromising computation time), over BEAT-MEV with naive virtualization, by ≈ 50× for 100 validators with total stake weight 5000 distributed as per the latest Solana stake distribution.