SilentLedger: Privacy-Preserving Auditing for Blockchains with Complete Non-Interactivity

arXiv:2509.08722 · cs.CR · Submitted 2025-09-10 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "SilentLedger: Privacy-Preserving Auditing for Blockchains with Complete Non-Interactivity".

Jane: The paper was written by Zihan Liu, Xiaohu Wang, Chao Lin, Minghui Xu, Debiao He et al. from Shandong University and Beihang University and Nanjing University of Aeronautics and Astronautics and Wuhan University.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Paper discussion segment 1: Tom: We are looking at "SilentLedger: Privacy-Preserving Auditing for Blockchains with Complete Non-Interactivity" by Zihan Liu and that whole team from Shandong University, Beihang, Nanjing University of Aeronautics and Astronautics, and Wuhan University.

Jane: It’s a huge collaborative effort across several major Chinese institutions to tackle the tension between keeping things private on a blockchain and making sure they can be audited if something goes wrong.

Tom: Jane, when you hear "complete non-interactivity" in that title, does it sound as revolutionary as it sounds to me?

Jane: It really is because most current systems require people to actually be online or actively participate when an auditor comes knocking. This paper suggests a way for transactions to happen silently and then be audited later without anyone needing to jump in and help.

Lu: I love how they are thinking about the structure of trust here, moving away from those clunky, interactive steps that slow everything down. If we can make auditing a background process that doesn's require user intervention, the scalability of decentralized finance could explode.

Meng: I have to play it safe for a second and ask if this actually works in a production environment without massive overhead. If every transaction requires these complex proofs, aren't we just trading one bottleneck for another?

Lalam: The cultural impact of that scalability is what strikes me most because it allows for seamless, private micro-transactions that don't disrupt the user experience. We could see a shift where digital privacy becomes a standard feature of every interaction rather than a specialized tool for tech experts.

Tom: That makes sense, but I wonder if the complexity is hidden in the math or in the actual data being sent over the network.

Jane: It’s definitely in the math, specifically how they use these renewable anonymous certificates to link identities without exposing them. We should probably look at what they are actually proposing to fix about existing models.

Paper discussion segment 2: Tom: So we've established that SilentLedger is aiming for this "silent" transaction style, but let's get into the meat of their summary and how they actually solve the problem.

Jane: They noticed that current privacy coins like Monero or Zerocash are great for privacy but they force people to interact with each other just to spend their own funds. SilentLedger wants to use things like Diffie-Hellman key exchanges and smart encryption so the payee can recover what they need without the payer being present.

Tom: Right, so it’s about making sure the transaction is "complete" from start to finish without a back-and-forth chat between parties.

Lu: And they aren't just stopping at making transactions easier for users; they are also solving the auditor's problem. They use a dual-account model where an auditor can look at the on-chain data and see who sent what and how much, but only if they have the right keys.

Meng: I was reading their comparison table, and it looks like they are claiming to be better than Traceable Monero or Platypus in terms of being non-interactive for both sides. How do they avoid the "single point of failure" issue where an auditor's node gets hacked and leaks everyone's data?

Lalam: That’s a huge technical hurdle, but they address it by using that renewable anonymous certificate scheme instead of relying on specific recording nodes. This means we don't have to trust one single entity to hold all the keys to our identity, which is a massive win for systemic stability.

Tom: It sounds like they've built a way to have your cake and eat it too—total privacy for the public, but total transparency for the law.

Jane: Exactly, and they are doing it by decoupling the auditing process from the transaction execution entirely. We should talk about how they actually implement these improvements to make it work on an elliptic curve.

Paper discussion segment 3: Tom: Now we're getting into the technical heavy lifting, where they explain how SilentLedger actually improves upon things like PEReDi or ACA.

Jane: The big improvement is that they avoid those massive computational costs you get with broadcast encryption. Instead of sending huge amounts of data to everyone, they use a clever structure that lets the payee and the auditor independently grab only what they need from the transaction.

Tom: I noticed they are working within elliptic curve environments, which is much more practical for modern blockchains than older RSA-style methods.

Lu: The way they handle "anonymous addresses" is brilliant because it uses a mathematical link to registered identities that can be renewed. This prevents people from making up fake identities just to dodge an audit, which has been a major flaw in other privacy designs.

Meng: I looked at their benchmarks and they're claiming superior performance, but I want to know about the transaction size. If we're adding all these proofs for authenticity and soundness, are we going to see massive bloat on the ledger?

Lalam: The paper shows that while there is a bit more data than a basic non-private transaction, it’s much more efficient than the state-of-the-art competitors they tested against. This efficiency is what will actually allow these privacy features to be integrated into real-world banking or IoT devices without breaking the system.

Tom: They even used Bulletproofs++ to keep those range proofs efficient, which is a pretty modern touch for ensuring amounts are valid without revealing them.

Jane: It’s a very tight design that manages to hit all those high notes—anonymity, confidentiality, and auditability—without making the user do any extra work. Let's wrap this up and see what the team thinks about the big picture.

Conclusion: Tom: We've covered a lot of ground with SilentLedger: Privacy-Preserving Auditing for Blockchains with Complete Non-Interactivity. It seems like a major step toward making blockchains usable for regulated industries.

Jane: It really is because it solves that fundamental conflict between the need for privacy and the necessity of compliance through non-interactive means.

Lu: I'm seeing a future where this kind of math becomes the backbone of all digital identity and value transfer, making "private but compliant" a standard reality.

Meng: From my side, if they can keep these performance gains as they scale to more complex transaction types, it's going to be a game-changer for enterprise blockchain adoption.

Lalam: Ultimately, this moves us toward a culture of "trustless compliance," where the system itself ensures honesty without requiring constant human oversight or invasive surveillance.

Tom: Well, that’s all the time we have for this one; thanks everyone for joining us. We'll be back soon with another deep dive into the latest research. Goodbye!

Jane: Bye everyone! Watch out for those next papers!

Shandong University · Beihang University · Nanjing University of Aeronautics and Astronautics · Wuhan University

cs.CR

Submitted: 2025-09-10

Updated: 2026-09-23

Code: https://github.com/herumi/mcl

Importance score: 83/100

The gist: The paper "SilentLedger: Privacy-Preserving Auditing for Blockchains with Complete Non-Interactivity" proposes a transaction system designed to reconcile blockchain privacy with compliance auditing

Key concepts

Complete Non-Interactivity: This means that transactions can happen silently without requiring people to be online or actively participate when an auditor arrives. It moves away from systems that require active user help during audits.
Dual-Account Model: SilentLedger uses a dual-account model where an auditor can review on-chain data, including who sent what and how much, but only if they possess the correct keys. This allows for auditing without needing constant interaction with the transaction parties.
Renewable Anonymous Certificates: These certificates are used to link identities in a way that maintains anonymity while allowing for renewal. This prevents users from creating fake identities to evade audits, addressing a major flaw in other privacy designs.

Terminology

Verification — 26× in this paper · explained in 2 episodes
Trace — 20× in this paper · explained in 2 episodes
Renewable Anonymous Certificate — 8× in this paper
Verifier — 7× in this paper · explained in 2 episodes
Cooperation — 6× in this paper · explained in 2 episodes
Core — 6× in this paper · explained in 2 episodes
Communication Overhead — 5× in this paper · explained in 2 episodes
Computational overhead — 5× in this paper · explained in 2 episodes
Benchmark — 3× in this paper · explained in 2 episodes
Decoupling — 3× in this paper · explained in 2 episodes
Accountability — 1× in this paper · explained in 2 episodes
Adaptability — 1× in this paper · explained in 1 episode
Benchmarking — 1× in this paper · explained in 3 episodes
Reversible Computing Function — 1× in this paper

Summary

The paper SilentLedger: Privacy-Preserving Auditing for Blockchains with Complete Non-Interactivity proposes a transaction system designed to reconcile blockchain privacy with compliance auditing through complete non-interactivity. The authors identify that existing auditable solutions often compromise usability and scalability by requiring interaction between users and auditors (e.g., requiring user cooperation to disclose amounts) or by creating runtime dependencies on the auditor during transaction execution (e.g., PEReDi).

To address these challenges, SilentLedger aims to achieve a state where users can transact without interaction, and auditors can audit without requiring cooperation from either party. The core contributions and technical components of the system include:

Complete Non-interactivity:

The system enables payers to finalize transactions without the payee online, and auditors can independently recover identities and amounts without user cooperation. This is intended to improve usability in asynchronous or resource-constrained settings like point-of-sale or IoT microtransactions.

Renewable Anonymous Certificate (RAC) Scheme:

To prevent identity forgery and sham transactions without relying on recording auditors, the authors introduce a renewable anonymous certificate (RAC) scheme with formal semantics and a rigorous security model. This mechanism allows for verifiably binding anonymous addresses to registered identities while preserving user privacy, ensuring that any holder of a valid certificate (C, σ) can independently choose r′ and derive an anonymous certificate (C′, σ′) without learning either the signing key x or the original random number r.

Novel Transaction Structure:

The authors design a structure that allows both payees and auditors to independently access the necessary transaction data using their respective keys, without requiring any interaction between parties. This structure is specifically designed to be compatible with elliptic curve–based blockchain environments, avoiding the computational overhead of broadcast encryption or multi-receiver encryption.

Formal Security and Implementation:

The paper provides formal security proofs including authenticity, anonymity, confidentiality, and soundness under a rigorous security model. The authors implement the system using the BLS12-381 curve via the mcl library. Experimental results demonstrate that SilentLedger achieves superior performance compared with state-of-the-art solutions, specifically noting that it incurs the lowest computational overhead for transaction generation among compared schemes and provides significant reductions in transaction generation overhead, validation time, and communication overhead relative to existing protocols like PEReDi.

The system operates through four distinct phases: (i) registration phase, (ii) transaction generation phase, (iii) transaction verification phase, and (iv) trace phase. Through these mechanisms, SilentLedger provides a privacy-preserving auditing system that achieves complete non-interactivity for both users and auditors while balancing privacy, compliance, and practicality.

Improvements for AI systems

To improve AI systems—specifically those operating in decentralized environments, automated financial agents, or autonomous IoT ecosystems—I would integrate the cryptographic architecture of SilentLedger.

Here are the specific improvements and their resulting capabilities:

Integrated Audit-Ready Autonomous Agents (DeFi/Automated Trading)

The paper solves the tension between total privacy and regulatory compliance through complete non-interactivity.

The Improvement: Implement a SilentLedger-based transaction layer within AI agent communication protocols. This replaces standard transparent or interactive privacy schemes with the Renewable Anonymous Certificate (RAC) and Reversible Computing Function (RF).
What the Improved AI Can Do: An autonomous trading bot can execute high-frequency, private trades on a blockchain without needing to be online to coordinate with a counterparty or an auditor. If the bot’s activity triggers a regulatory flag, an authorized regulator can independently recover the identity and amount from on-chain data without requiring the AI agent (or its developer) to interact or cooperate, preventing denial-of-service attacks via audit requests.

Privacy-Preserving Verifiable Machine Learning (ZKP + SilentLedger)

The paper introduces a novel transaction structure that allows independent decryption of specific data subsets using separate keys (payee vs. auditor).

The Improvement: Integrate SilentLedger’s Novel Transaction Structure into Decentralized AI Inference networks. Instead of sending raw data to a model, users send encrypted inputs where the decryption key is split via the paper's AKE/SKE mechanism.
What the Improved AI Can Do: It enables Zero-Knowledge Inference. An AI provider can prove that a specific model was run on specific private data without seeing the data, while an auditor can verify that the transaction amounts (or data usage costs) are legitimate. Because of the complete non-interactivity, these verification proofs can be validated by the blockchain asynchronously, allowing for massive scalability in decentralized compute markets.

Secure Decentralized IoT Orchestration

The paper addresses the offline payee problem, which is critical for resource-constrained devices.

The Improvement: Deploy SilentLedger’s Non-interactive AAGen (Anonymous Account Generation) as the standard for micro-payments in IoT mesh networks.
What the Improved AI Can Do: An AI-driven smart grid or autonomous vehicle fleet can engage in sub-second, micro-scale transactions without a constant handshake. An autonomous drone can pay a charging station via an anonymous account; even if the drone is offline or moving through a low-connectivity zone, the transaction remains valid and auditable. The auditor can trace the drone's identity only if legally mandated, but the transaction execution itself never stalls due to interaction latency.

Tamper-Proof Decentralized Identity (DID) for AI

The paper’s RAC scheme allows for signature-adaptable anonymous identities that are verifiably linked to a long-term identity without exposing it.

The Improvement: Use the RAC scheme as the foundation for AI Agent Identity Management.
What the Improved AI Can Do: It enables Verifiable Anonymity. An AI agent can prove it is a registered and authorized entity (via the certificate) to participate in a smart contract, yet it can generate infinite one-time anonymous addresses for every single action. This prevents an adversary from tracking an AI's entire behavioral history through its address, while still ensuring that if the AI behaves maliciously, its legal owner can be identified via the auditor’s management key.