Xiao Wang

Reserved ip, disk, template, gateway all cost money 🥲 disk with customized up cost money even if you don’t use it I think

Actually you should ask all your followers to point to your website!

Ask all your (ex)students to point to your maybe

Sorry, your protocol cannot be secure. I will find a bug after the rebuttal.

See left bottom corner, it says 0 caffeine😉

What can we do without random oracles 🥲

You need random oracle instead? Collision resistance may have output containing some input.

Abstract. Oblivious RAMs (ORAMs) allow data outsourcing to servers so that the access pattern to the outsourced data is kept private. It is also a crucial building block to enable private RAM access within secure multi-party computation (MPC). In recent years, schemes that match the ORAM lower bound have been proposed in both the outsourcing setting and the RAM-model MPC setting, seemingly putting an epilogue in the theory of ORAM. In this paper, we initiate a study of mixed-mode ORAMs, where accesses to the ORAM are a mix of both public and private accesses. Although existing ORAMs can support public access by treating them as private ones, achieving better efficiency is highly non-trivial. - We present a mixed-mode ORAM algorithm, assuming the existence of private information retrieval (PIR). When the PIR scheme is communication-efficient, this ORAM achieves the best possible outcome: it has a bandwidth blowup of O(logN) for private accesses and O(1) for public accesses. This construction can be easily extended for the MPC setting achieving O(BlogN) circuit size for private accesses to B-sized blocks and O(B) circuit size for public accesses to the same array. - We instantiate the above protocol in the three-party computation (3PC) setting with more concrete optimizations, yielding a protocol that performs almost as efficiently as state-of-the-art RAM-3PC protocols for private accesses while being 3× more efficient for public accesses in the LAN setting.

Image showing part 2 of abstract.

Efficient Mixed-Mode Oblivious RAMs (Wenhao Zhang, Xiao Wang, Chenkai Weng) ia.cr/2025/1057

Abstract. Private signaling allows servers to identify a recipient’s messages on a public bulletin board without knowing the recipient’s metadata. It is a central tool for systems like privacy-preserving blockchains and anonymous messaging. However, unless with TEE, current constructions all assume that the servers are only passively corrupted, which significantly limits their practical relevance. In this work, we present a TEE-free simulation-secure private signaling protocol assuming two non-colluding servers, either of which can be actively corrupted. Crucially, we convert signal retrieval into a problem similar to private set intersection and use custom-built zero-knowledge proofs to ensure consistency with the public bulletin board. As a result, our protocol achieves lower server-to-server communication overhead and a much smaller digest compared to state-of-the-art semi-honest protocol. For example, for a board size of 2¹⁹ messages, the resulting digest size is only 33.57KB. Our protocol is also computationally efficient: retrieving private signals only takes about 2 minutes, using 16 threads and a LAN network.

Image showing part 2 of abstract.

Private Signaling Secure Against Actively Corrupted Servers (Haotian Chu, Xiao Wang, Yanxue Jia) ia.cr/2025/1056

Abstract. Differentially private stochastic gradient descent (DP-SGD) trains machine learning (ML) models with formal privacy guarantees for the training set by adding random noise to gradient updates. In collaborative learning (CL), where multiple parties jointly train a model, noise addition occurs either (i) before or (ii) during secure gradient aggregation. The first option is deployed in distributed DP methods, which require greater amounts of total noise to achieve security, resulting in degraded model utility. The second approach preserves model utility but requires a secure multiparty computation (MPC) protocol. Existing methods for MPC noise generation require tens to hundreds of seconds of runtime per noise sample because of the number of parties involved. This makes them impractical for collaborative learning, which often requires thousands or more samples of noise in each training step. We present a novel protocol for MPC noise sampling tailored to the collaborative learning setting. It works by constructing an approximation of the distribution of interest which can be efficiently sampled by a series of table lookups. Our method achieves significant runtime improvements and requires much less communication compared to previous work, especially at higher numbers of parties. It is also highly flexible – while previous MPC sampling methods tend to be optimized for specific distributions, we prove that our method can generically sample noise from statistically close approximations of arbitrary discrete distributions. This makes it compatible with a wide variety of DP mechanisms. Our experiments demonstrate the efficiency and utility of our method applied to a discrete Gaussian mechanism for differentially private collaborative learning. For 16 parties, we achieve a runtime of 0.06 seconds and 11.59 MB total communication per sample, a 230× runtime improvement and 3× less communication compared to the prior state-of-the-art for sampling from discrete Gaussian distribution in MPC.

Image showing part 2 of abstract.

Secure Noise Sampling for Differentially Private Collaborative Learning (Olive Franzese, Congyu Fang, Radhika Garg, Somesh Jha, Nicolas Papernot, Xiao Wang, Adam Dziedzic) ia.cr/2025/1025

Performance is secondary.

If you are submitting to @ieeessp.bsky.social
this year, a friendly reminder that there is an abstract submission deadline this Thursday May 29 (AoE).

More details: sp2026.ieee-security.org/cfpapers.html

They never thought anyone would pay?

Decision-making studies appear in HCI, vis, & increasingly AI/ML, but how “good decision” is defined is often ad-hoc

My #CHI2025 talk today will answer Qs like:
What's a decision problem?
What's the best possible performance on a decision problem?
What minimum info must participants be given?

1/2

Abstract. The Nintendo DSi is a handheld gaming console released by Nintendo in 2008. In Nintendo’s line-up the DSi served as a successor to the DS and was later succeeded by the 3DS. The security systems of both the DS and 3DS have been fully analysed and defeated. However, for over 14 years the security systems of the Nintendo DSi remained standing and had not been fully analysed. To that end this work builds on existing research and demonstrates the use of a second-order fault injection attack to extract the ROM bootloaders stored in the custom system-on-chip used by the DSi. We analyse the effect of the induced fault and compare it to theoretical fault models. Additionally, we present a security analysis of the extracted ROM bootloaders and develop a modchip using cheap off-the-shelf components. The modchip allows to jailbreak the console, but more importantly allows to resurrect consoles previously assumed irreparable.

An in-depth security evaluation of the Nintendo DSi gaming console (pcy Sluys, Lennert Wouters, Benedikt Gierlichs, Ingrid Verbauwhede) ia.cr/2025/568

@iacrcrypto.bsky.social allows remote attendance/presentation this year!

YouTube video by That Good News Girl Beavers outperform government, completing 7-year project in 2 days #shorts #beavers

I will play it next time I need to give a talk on Beaver Triples youtu.be/h7aEJMW9J1g?...

I think only the egg one was intended. Others are really just because of too much undergrad teaching!

I remembered my host told me that DJI dropped 666points on the day of my first interview. I told him yes, drone business is difficult…

Congrats!

Congrats!!

Crypto 2025 Crypto 2025

If you need a visa to attend Crypto 2025 (crypto.iacr.org/2025/) please make sure to start the process early this year by reaching out the general chair Francisco Rodríguez Henríquez at crypto2025@iacr.org

Abstract. Zero-Knowledge Succinct Non-interactive Arguments of Knowledge (zkSNARKs) lead to proofs that can be succinctly verified but require huge computational resources to generate. Prior systems outsource proof generation either through public delegation, which reveals the witness to the third party, or, more preferably, private delegation that keeps the witness hidden using multiparty computation (MPC). However, current private delegation schemes struggle with scalability and efficiency due to MPC inefficiencies, poor resource utilization, and suboptimal design of zkSNARK protocols. In this paper, we introduce DFS, a new zkSNARK that is delegation-friendly for both public and private scenarios. Prior work focused on optimizing the MPC protocols for existing zkSNARKs, while DFS uses co-design between MPC and zkSNARK so that the protocol is efficient for both distributed computing and MPC. In particular, DFS achieves linear prover time and logarithmic verification cost in the non-delegated setting. For private delegation, DFS introduces a scheme with zero communication overhead in MPC and achieves malicious security for free, which results in logarithmic overall communication; while prior work required linear communication. Our evaluation shows that DFS is as efficient as state-of-the-art zkSNARKs in public delegation; when used for private delegation, it scales better than previous work. In particular, for 2²⁴ constraints, the total communication of DFS is less than 500KB, while prior work incurs 300GB, which is linear to the circuit size. Additionally, we identify and address a security flaw in prior work, EOS (USENIX’23).

Image showing part 2 of abstract.

DFS: Delegation-friendly zkSNARK and Private Delegation of Provers (Yuncong Hu, Pratyush Mishra, Xiao Wang, Jie Xie, Kang Yang, Yu Yu, Yuwen Zhang) ia.cr/2025/296

I would finally like to thank the selection committee for recognizing my work, and I look forward to leveraging this fellowship to further advance my research and contribute to our community.

It is such an honor to be selected as a #SloanFellow! I want to thank my department, collaborators, mentors, students, and my family for their inspiration and support! I am also particularly grateful to NSF for their ongoing support, which has been instrumental to my research.

Wait until you try Teams.

The BitGC project started in 2023, but it took us more than a year to get it accepted. Never give up on great ideas!!

In BitGC, we introduce the first concretely efficient garbling with one bit per gate. It only requires constant number of SWHE operations per gate without any boostrapping. In this new work, we design a way to bring active security without sacrificing the concrete efficiency (no more than 2X!).

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