A layered system model and an analysis pipeline that uses prior history as the temporal baseline, applies cumulative pruning and cross-proof propagation to each proof's Commitment Set, and recursively traces the survivors through historical hidden-state transitions to derive the final transaction-level Anonymity Set Size is proposed.
Abstract
Shielded UTXO-based protocols are becoming a core form of privacy infrastructure for DeFi. Unlike mixers that organize privacy mainly around deposits and withdrawals, these protocols allow assets, once inside the shielded pool, to continue moving and being re-spent within the hidden state, and to become public only when users withdraw or interact with public DeFi protocols. Their anonymity is therefore no longer a flat pool-size problem, but a provenance problem that propagates across the note/UTXO, proof, and transaction layers. Yet, a unified analysis framework for this setting is still missing. We propose a layered system model and an analysis pipeline that uses prior history as the temporal baseline, applies cumulative pruning and cross-proof propagation to each proof's Commitment Set, and recursively traces the survivors through historical hidden-state transitions to derive the final transaction-level Anonymity Set Size. We evaluate our methodology on the complete on-chain histories of all four Railgun production deployments and five independent Hinkal pools across six EVM chains, analyzing 186,356 unshielding spend transactions. Using only public protocol traces and constraints, our non-heuristic analysis yields mean Anonymity Set Size reductions of 40.1%-59.0% relative to each deployment's temporal baseline; 3,679 transactions retain at most 10 addresses, including 1,228 singletons. Public token constraints are the strongest and most stable source of pruning in both protocols, while the effects of tree number, proof roots, and value constraints vary with protocol design and historical state. Together with representative cases, these results reveal interpretable anonymity-loss patterns and implications for user behavior and future protocol design.
This work introduces minimal information disclosure (MID), which designs and quantifies the information content of verifier-facing evidence itself and measures collateral leakage with conditional mutual information.
Large Language Models (LLMs) such as ChatGPT, Gemini, Claude, etc. have become integral tools for technical writing, software development, and communication. However, these systems, despite privacy assurances, still expose a significant privacy risk. User prompts often contain personally identifiable or proprietary information that is transmitted in plain-text to external providers, where it may be logged or accessed despite opt-out policies. This research aims to address this gap by designing a lightweight local encryption middleware that acts as a privacy firewall between users and remote AI APIs. Before a message is sent, the middleware automatically detects sensitive entities (Personally Identifiable Information or PII) and replaces them with typed placeholders such as [PERSON_1] or [ORG_1]. A local mapping (with optional authenticated encryption at rest) links these placeholders to their original values, allowing the system to reconstruct (rehydrate) the final response once the model replies. This design aims to reduce the exposure of sensitive information in human-readable form while maintaining the AI output. We evaluate the system in terms of detection accuracy, reconstruction fidelity, and utility preservation, demonstrating a practical solution for privacy-preserving LLM interaction.
Aditya Vishwakarma, Wencen Wu· International Conference on...· 0 citations
Privacy-preserving payment systems are well understood, yet their adoption in regulated settings, such as central bank digital currencies (CBDCs), institutional stablecoins, and other compliant payment infrastructures, has been limited by concerns over their potential misuse for illicit activities. Regulators counter financial crime with a toolbox of complementary measures to identify, trace, and stop criminal actors. Tracing is one key tool: acting on outside evidence that a user is implicated in a crime such as money laundering, law enforcement follows the suspect's funds through the ledger to uncover laundering routes and accomplices. The tracing schemes proposed in the literature, however, grant authorities unbounded capabilities: once initiated, tracing propagates through the transaction graph or persists across all future transactions of a user, and may eventually deanonymize the entire ledger. Only the goodwill of the authority, or the honesty of a committee, keeps surveillance targeted and temporary. We introduce ephemeral coin tracing (ECT), a primitive whose tracing capacity is bounded by construction, both in the number of simultaneously traced users and in the number of hops each trace survives. The authority issues tracing tags that degrade at each hop; after a protocol-defined number of hops, a tag collapses into a value indistinguishable from that of an untagged coin. Within a tracing period the bound is absolute: no authority, however motivated, can follow a tag past its budget. We formalize ECT, define its security and privacy guarantees, and give two constructions, one over exponential ElGamal and one over Damg{\aa}rd--Jurik encryption.
Ignacio Amores-Sesar, Christian Cachin, Rohit Chatterjee et al.· 0 citations
Received wisdom on payments infrastructure strongly supports the custodial, account-based model as a necessity for transaction integrity, auditability and verification; the set of fundamental primitives for regulated digital money exchange, the argument goes, necessitates designated identifiable entities that store and process credentials, perform KYC, and ultimately act as the'single version of the truth'for compliance remediation and, most important, AML. In this paper, we propose this is not the case, by arguing that non-custodial, cash-like digital assets can embody such capabilities, in an arguably more secure manner. To that end, we present a reference architecture and core protocol rules for digital-value-exchange systems that preserve meaningful user privacy while enabling strong auditability. The protocol defines the conditions under which digital asset creation, transfer, and redemption are valid. The architecture specifies the allocation of actors, roles and components through which these rules operate, enabling independent verification of transaction compliance with applicable norms. Building upon the Unforgeable, Stateful, Oblivious (USO) asset model of Goodell et al., regulatory compliance data are embedded directly into the asset state as cryptographically signed attestations issued by independent entities. A transfer is valid only upon satisfaction of applicable compliance predicates and inclusion of the resulting signature within the asset state. Compliance enforcement is thus performed at the protocol level rather than through institutional custody or identity-based account control. We conclude that our proposed model can successfully interface with existing payment systems, making it possible to integrate non-custodial, compliance-verified transactions with legacy financial infrastructure.
Santiago De Simone, Geoffrey Goodell, G. Samakovitis· 0 citations
Cross-domain data flows are commonplace in regulated inter-organizational environments, where durable audit evidence must be retained without publicly exposing sensitive flow metadata. This paper presents a privacy-preserving on-chain attestation framework for recorded cross-domain data transfers in a permissioned setting. Its core data structure, termed GBFPlus, extends the Garbled Bloom Filter (GBF) with explicit occupancy indicators, constrained payloads that encode a consistency prefix and an adjacent-domain identifier, and distinct pairing-derived positions. Each domain administrator records observed inbound and outbound transfers in directional GBFPlus instances and periodically commits signed filter attestations to an append-only ledger. An authorized regulator can reconstruct candidate transfer edges from available bilateral attestations, while light clients verify ledger inclusion through Merkle proofs. A traceable anonymous attestation signature conceals the uploader’s cryptographic identity from ordinary ledger observers while retaining regulator-assisted accountability. The security analysis establishes integrity, conditional anonymity, traceability, and metadata-privacy properties for committed attestations under the stated trust assumptions, and the prototype evaluation reports the measured costs of GBFPlus and the signature operations.
Sihang Qin, Yang Zhou, Weiqi Dai et al.· Entropy· 0 citations
A privacy-preserving zk-SNARK-based audit framework that searches for probes designed in the spirit of adversarial examples to amplify logit drift between an approved model and a modified deployment and demonstrates that token-based probes consistently deliver the strongest mean sensitivity across models and GPU platforms, although operating in a black-box setting.
Cameron Wilding, Mina Shaker, Fatemeh Ganji· 0 citations
