Blocklisted Oblivious Pseudorandom Functions

• URL: http://arxiv.org/abs/2507.16040v1
• Date: Mon, 21 Jul 2025 20:13:50 GMT
• Title: Blocklisted Oblivious Pseudorandom Functions
• Authors: Xinyuan Zhang, Anrin Chakraborti, Michael Reiter,
• Abstract summary: An oblivious pseudorandom function (OPRF) is a protocol by which a client and server interact to evaluate a pseudorandom function.<n>We extend this notion by enabling the server to specify a blocklist, such that OPRF evaluation succeeds only if the client's input is not on the blocklist.
• Score: 5.230596278040867
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: An oblivious pseudorandom function (OPRF) is a protocol by which a client and server interact to evaluate a pseudorandom function on a key provided by the server and an input provided by the client, without divulging the key or input to the other party. We extend this notion by enabling the server to specify a blocklist, such that OPRF evaluation succeeds only if the client's input is not on the blocklist. More specifically, our design gains performance by embedding the client input into a metric space, where evaluation continues only if this embedding does not cluster with blocklist elements. Our framework exploits this structure to separate the embedding and blocklist check to enable efficient implementations of each, but then must stitch these phases together through cryptographic means. Our framework also supports subsequent evaluation of the OPRF on the same input more efficiently. We demonstrate the use of our design for password blocklisting in augmented password-authenticated key exchange, and to MAC only executables that are not similar to ones on a blocklist of known malware.

Related papers

• Reverse Online Guessing Attacks on PAKE Protocols [0.43748379918040853]
  We show that password-authenticated key exchange protocols are vulnerable to reverse online guessing attacks.<n> reverse guessing poses a unique risk because the burden of detection is shifted to the clients.<n>Our analysis suggests that stakeholders should, by default, authenticate the server using more stringent measures than just the user's password.
  arXiv  Detail & Related papers  (2026-02-09T18:40:53Z)
• Proof of Trusted Execution: A Consensus Paradigm for Deterministic Blockchain Finality [0.391985484065646]
  We propose Proof of Trusted Execution (PoTE), a consensus paradigm where agreement emerges from verifiable execution rather than replicated re-execution.<n>Because the execution is deterministic and the proposer is uniquely derived from public randomness, PoTE avoids forks, eliminates slot.time bottlenecks, and commits blocks in a single round of verification.
  arXiv  Detail & Related papers  (2025-12-10T08:04:38Z)
• Federated Anonymous Blocklisting across Service Providers and its Application to Group Messaging [1.7616042687330637]
  In Anonymous Blocklisting schemes, users must prove during authentication that none of their previous pseudonyms has been blocked.<n>We propose an alternative textitFederated Anonymous Blocklisting (FAB) in which the centralised Service Provider is replaced by small distributed Realms.
  arXiv  Detail & Related papers  (2025-11-05T14:11:46Z)
• Flexible Threshold Multi-client Functional Encryption for Inner Product in Federated Learning [6.909482184241419]
  Federated learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train a shared model without disclosing their local data.<n>In this paper, we design a flexible threshold multi-client functional encryption for inner product (FTMCFE-IP) scheme.
  arXiv  Detail & Related papers  (2025-10-17T06:58:16Z)
• ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version) [58.595691399741646]
  Split Learning (SL) is a distributed learning approach that enables resource-constrained clients to collaboratively train deep neural networks (DNNs)<n>This setup enables SL to leverage server capacities without sharing data, making it highly effective in resource-constrained environments dealing with sensitive data.<n>We present ZORRO, a private, verifiable, and robust SL defense scheme.
  arXiv  Detail & Related papers  (2025-09-11T18:44:09Z)
• Blockwise SFT for Diffusion Language Models: Reconciling Bidirectional Attention and Autoregressive Decoding [60.06816407728172]
  Discrete diffusion language models have shown strong potential for text generation.<n>Standard supervised fine-tuning misaligns with semi-autoregressive inference.<n>We propose Blockwise SFT, which partitions responses into fixed-size blocks.
  arXiv  Detail & Related papers  (2025-08-27T02:49:33Z)
• Towards Anonymous Neural Network Inference [0.0]
  funion is a system providing end-to-end sender-receiver unlinkability for neural network inference.<n>Users can anonymously store input tensors in pseudorandom storage locations, commission compute services to process them via the neural network, and retrieve results with no traceable connection between input and output parties.
  arXiv  Detail & Related papers  (2025-05-23T22:05:20Z)
• The Latency Price of Threshold Cryptosystem in Blockchains [52.359230560289745]
  We study the interplay between threshold cryptography and a class of blockchains that use Byzantine-fault tolerant (BFT) consensus protocols.<n>Our measurements from the Aptos mainnet show that the optimistic approach reduces latency overhead by 71%.
  arXiv  Detail & Related papers  (2024-07-16T20:53:04Z)
• Asymmetric Mempool DoS Security: Formal Definitions and Provable Secure Designs [17.06992341258962]
  This paper introduces secure blockchain-mempool designs capable of defending against any form of asymmetric eviction DoS attacks. Our proposed secure transaction admission algorithm, named textscsaferAd-CP, ensures eviction-security by providing a provable lower bound on the cost of executing eviction DoS attacks.
  arXiv  Detail & Related papers  (2024-07-03T23:28:35Z)
• Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing [9.881799766856476]
  We introduce a certifiable defense against patch attacks that guarantees, for a given executable and an adversarial patch size, no adversarial EXEmple exist. Our method is inspired by (de)randomized smoothing which provides deterministic robustness certificates. Results show that our method exhibits unmatched robustness against strong content-insertion attacks, outperforming randomized smoothing-based defenses in the literature.
  arXiv  Detail & Related papers  (2024-05-01T08:45:57Z)
• Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing [107.97160023681184]
  Aligned large language models (LLMs) are vulnerable to jailbreaking attacks. We propose SEMANTICSMOOTH, a smoothing-based defense that aggregates predictions of semantically transformed copies of a given input prompt.
  arXiv  Detail & Related papers  (2024-02-25T20:36:03Z)
• Blind Evaluation Framework for Fully Homomorphic Encryption and Privacy-Preserving Machine Learning [0.0]
  Blind Evaluation Framework (BEF) is a cryptographically secure programming framework. It enables blind, but correct, execution of programming logic without Interactive Rounds of Decryption and Evaluation (IRDE) This is the first framework to enable both training and inference of machine learning models with FHE without decryption rounds.
  arXiv  Detail & Related papers  (2023-10-19T20:33:02Z)
• FLCert: Provably Secure Federated Learning against Poisoning Attacks [67.8846134295194]
  We propose FLCert, an ensemble federated learning framework that is provably secure against poisoning attacks. Our experiments show that the label predicted by our FLCert for a test input is provably unaffected by a bounded number of malicious clients.
  arXiv  Detail & Related papers  (2022-10-02T17:50:04Z)
• Blockchain-based Secure Client Selection in Federated Learning [18.001794899303626]
  We use blockchain technology to propose a verifiable client selection protocol for Federated Learning. Our protocol enforces a random selection of clients, making the server unable to control the selection process at its discretion. We present security proofs showing that our protocol is secure against this attack.
  arXiv  Detail & Related papers  (2022-05-11T16:28:12Z)
• Quantum Proofs of Deletion for Learning with Errors [91.3755431537592]
  We construct the first fully homomorphic encryption scheme with certified deletion. Our main technical ingredient is an interactive protocol by which a quantum prover can convince a classical verifier that a sample from the Learning with Errors distribution in the form of a quantum state was deleted.
  arXiv  Detail & Related papers  (2022-03-03T10:07:32Z)
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients [124.48732110742623]
  We propose a novel framework by integrating blockchain into Federated Learning (FL) BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning. It gives rise to a new problem of training deficiency, caused by lazy clients who plagiarize others' trained models and add artificial noises to conceal their cheating behaviors.
  arXiv  Detail & Related papers  (2020-12-02T12:18:27Z)
• Towards Bidirectional Protection in Federated Learning [70.36925233356335]
  F2ED-LEARNING offers bidirectional defense against malicious centralized server and Byzantine malicious clients. F2ED-LEARNING securely aggregates each shard's update and launches FilterL2 on updates from different shards. evaluation shows that F2ED-LEARNING consistently achieves optimal or close-to-optimal performance.
  arXiv  Detail & Related papers  (2020-10-02T19:37:02Z)
• FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning [18.237186837994585]
  A'secure aggregation' protocol enables the server to aggregate clients' models in a privacy-preserving manner. FastSecAgg is efficient in terms of computation and communication, and robust to client dropouts.
  arXiv  Detail & Related papers  (2020-09-23T16:49:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.