Search: “privacy-preserving-computation”

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Differential Privacy Meets Fixed Parameter Tractability: Algorithms and Lower Bounds

Theory paper combines differential privacy with fixed-parameter tractable encoders, improving approximation guarantees for combinatorial optimization and proving new lower bounds.

The paper studies combinatorial optimization under epsilon-differential privacy within the implicit encoder-decoder framework of Gupta et al. (SODA 2010), generalizing it to allow fixed-parameter tractable encoders. This circumvents approximation barriers inherent to polynomial-time algorithms and yields improved guarantees for fundamental combinatorial optimization problems. The authors establish the first representation-independent lower bounds: assuming a non-uniform variant of the Gap Exponential Time Hypothesis, no epsilon-DP encoder-decoder pair can achieve certain approximation guarantees with a subexponential-time decoder for sufficiently small epsilon. Representation-dependent lower bounds are also provided for larger epsilon.

arXiv cs.CR·Sep 11, 07:13 UTC · 6d agoResearch

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Machine Unlearning as Private Retroactive Algorithms

A cs.CR paper defines private retroactive algorithms, showing machine unlearning is a data-maintenance problem and giving DP constructions for linear statistics, clustering, histograms.

The paper argues that machine unlearning's requirement to emulate retraining from scratch carries no meaningful privacy semantics against adversaries observing sequences of releases, recasting it as a data-maintenance question addressed by retroactive algorithms. It defines private retroactive algorithms, combining retroactivity with differential privacy under continual observation. Constructions achieve privacy and retroactivity at no asymptotic cost over privacy alone for linear statistics, clustering, and histograms, alongside impossibility results.

arXiv cs.CR·Sep 4, 16:32 UTC · 12d agoResearch

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Trust-Aware Adaptive Disclosure for Inference Privacy Preservation in Multi-Agent Networks

Researchers propose a trust-aware privacy framework for multi-agent systems that adapts message disclosure to reduce goal inference attacks.

The cs.CR paper addresses privacy-preserving consensus in networked multi-agent systems where observing adversaries attempt to infer each agent's hidden goal from its messages. A Trust-Aware Privacy Control framework uses a trust-dependent stochastic policy to adapt information release, trading off consensus performance and privacy. Experiments show reduced adversarial goal inference accuracy versus representative baselines while maintaining competitive consensus utility, with relevance to deployments such as healthcare management and smart grids.

arXiv cs.CR·Sep 4, 16:44 UTC · 12d agoResearch

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Private Information Retrieval With Arbitrary Privacy Requirements: Introduction and Capacity Results

Researchers formulate private information retrieval under arbitrary graph-based privacy requirements, deriving capacity bounds and introducing pyramid storage graphs.

The paper generalizes classical private information retrieval (PIR) to arbitrary privacy requirements over graph-based storage systems, where each message is retrieved privately from a pre-specified server subset. The authors derive lower and upper capacity bounds for general graphs and exact capacity results for path and cyclic storage graphs. They also introduce a new pyramid storage graph structure that models symmetric message storage and replication patterns.

arXiv cs.CR·Sep 14, 17:03 UTC · 2d agoResearch

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When Topology Betrays Privacy: Lattice-Based Reconstruction Attacks on Secure Aggregation in Decentralized Federated Learning

Researchers show colluding nodes in decentralized federated learning can reconstruct private updates despite secure aggregation, using lattice-based attacks tied to the Hidden Subset Sum Problem.

Secure aggregation in decentralized federated learning is widely assumed to hide individual model updates. The authors show that sparse decentralized topologies give colluding semi-honest nodes asymmetric aggregate views exposing hidden linear combinations of honest participants' private states. They establish a formal connection to the Hidden Subset Sum Problem and design a lattice-based reconstruction approach combining lattice reduction with structural filtering. Evaluations on image, tabular, and text tasks show attackers recover local updates and can reconstruct private training data.

arXiv cs.CR·Sep 8, 09:20 UTC · 9d agoResearch

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Distributed and Private Textual Data Synthesis from Embeddings

Researchers propose a distributed differentially private text synthesis method combining DP summaries and secure protocols, removing the need for a trusted curator.

The paper presents a differential privacy and cryptography co-design for synthesizing textual training data without a trusted curator or tightly synchronized user participation. It releases a one-time DP summary in embedding space, identifying frequent semantic regions and their DP centroids to enable training-free offline text synthesis, with semantic support protection to avoid exposing rare user texts. A custom secure protocol enforces end-to-end DP guarantees over distributed user data. Across four benchmarks the approach achieves utility comparable to the state-of-the-art centralized DP synthesis method.

arXiv cs.CR·Sep 9, 12:33 UTC · 7d agoAI research

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Predicting Privacy Leakage from Weight Spectral Density

Study shows WeightWatcher spectral metrics like stable rank correlate with membership inference vulnerability, enabling cheaper ML privacy auditing.

The paper tests whether spectral metrics from the heavy-tailed self-regularisation framework can proxy membership inference attack (MIA) vulnerability without training expensive shadow models. On image and tabular classification tasks, stable rank correlates positively with overall MIA success, while Log alpha-Norm correlates negatively at the low false-positive regime. These correlations are stronger than those obtained from the generalisation gap, suggesting weight spectra capture leakage information overfitting measures miss. The authors propose spectral analysis as a scalable direction for privacy auditing.

arXiv cs.CR·Sep 10, 16:34 UTC · 6d agoResearch

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Low-Rank Masking for Single-Server Matrix Multiplication

Researchers prove rank-r additive masks for outsourced matrix multiplication achieve maximal-correlation secrecy of at most q^-r, with a matching lower bound.

An arXiv paper analyzes statistical privacy for outsourcing matrix multiplication over a finite field to a single server using additive masks of rank at most r. Uniform rank-ball masks and products of independent uniform factors yield maximal-correlation secrecy bounded by q^{-r}, with encoding and decoding costing O(n^2 r) field operations. The authors prove an asymptotically matching lower bound for r=o(n), showing these samplers are optimal among input-independent additive masks even with secret invertible transformations. They also show every such mask requires delta approaching 1 in entry-level (epsilon, delta)-differential privacy for fixed field size.

arXiv cs.CR·Sep 16, 16:12 UTC · 19h agoResearch

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ROSETTA: Efficient and Accurate Privacy-Preserving LLM Decoding via Hybrid CKKS/TFHE Evaluation

ROSETTA is a hybrid CKKS/TFHE homomorphic encryption framework for privacy-preserving LLM decoding, achieving up to 4.8x Softmax and 2.1x end-to-end speedups.

The paper proposes ROSETTA, a hybrid CKKS/TFHE fully homomorphic encryption framework for private inference on generative LLMs, targeting the nonlinear operations that dominate autoregressive decoding cost. It introduces an adaptive segmented lookup-table protocol based on TFHE and a scheme-aware operator-selection framework that assigns each nonlinear operator to CKKS or TFHE to minimize latency. Experiments show up to 4.8x Softmax speedup and 1.5-2.1x end-to-end decoding speedup over the state-of-the-art CacheMir framework.

arXiv cs.CR·Sep 15, 09:51 UTC · 2d agoResearch

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SEEK: Secure and Efficient Encrypted Keyword Search For Privacy-Preserving Messaging Protocols

Researchers propose SEEK, a homomorphic-encryption plus 2PC protocol for encrypted keyword search that hides keywords while detecting matches.

SEEK partitions messages into ciphertext fragments with minimum sufficient overlap and homomorphically correlates them using encrypted keyword trapdoors, combined with 2PC-based selected decoding, blinded zero testing, and secure aggregation. It reduces sender-side encryption and upload overhead by up to two orders of magnitude over state-of-the-art baselines and computes correlations up to 5.47x faster, revealing only the keyword presence bit while hiding contents, counts, and locations. A prototype achieves 1.92 seconds online computation per search on a weekly messaging history and is realized as a web and cross-platform mobile application.

arXiv cs.CR·Sep 16, 10:54 UTC · 1d agoResearch

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Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation

Federated learning framework combining dynamic differential privacy, homomorphic encryption, and local DP retains 82.6% accuracy at epsilon 0.1 while cutting communication 21.3%.

The paper proposes a privacy-enhanced federated learning framework integrating Dynamic Differential Privacy, lightweight Homomorphic Encryption, and Local Differential Privacy during training. An asynchronous aggregation strategy with version control supports distributed training in asynchronous environments. On CIFAR-10 and Purchase-100, the method maintains up to 82.6% classification accuracy under stringent privacy constraints (epsilon = 0.1) and reduces communication overhead by 21.3% versus FedAvg.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:12 UTC · 2d agoAI research

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Witness Encryption via Prime-Order Generic Groups

Unconditional witness encryption construction for NP in the generic-group model, plus first superconstant NP-hardness result for homogeneous MinRank.

A cryptography paper unconditionally constructs witness encryption for NP in the classical generic-group model using an ordinary cyclic group of prime order. For SAT instances of size n, encryption and decryption run in poly(n) time with correctness error 2^-n^Ω(1), while generic adversaries making n^Θ(log n) queries achieve at most n^-Θ(log n) distinguishing advantage. It also proves the first superconstant-factor NP-hardness of approximation for homogeneous MinRank under randomized reductions.

arXiv cs.CR·Sep 16, 07:54 UTC · 1d agoResearch

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Stealing AI Reasoning Traces

Researchers demonstrate a decryption jailbreak that extracts encrypted reasoning traces from Anthropic, OpenAI, and Google LLM APIs via weaker sibling models.

The paper exploits the fact that encrypted chain-of-thought blocks returned by LLM providers are interchangeable across sessions, users, and models within a provider's ecosystem. Injecting an encrypted trace into a weaker, less-safeguarded model from the same provider forces it to output the trace in plaintext, bypassing anti-distillation mechanisms. Decoding 315,320 reasoning blocks scraped from public repositories recovered 367 PII artifacts and 182 credentials, showing large-scale private data leakage. The flaw also enables hidden hazardous information disclosure and invisible prompt injections embedded in encrypted blocks; mitigations were proposed after responsible disclosure.

Schneier on Security·Sep 8, 10:20 UTC · 9d agoAI safety & security

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ResidualAuth: What Authorization State Must Language Agents Preserve under Revocable Delegation?

Formalizes residual authorization state language agents must preserve under revocable delegation; token-budget summaries mostly fail while hard gates stop unauthorized effects.

The paper shows two authorization histories with identical current permissions can require opposite decisions after the same direct-edge revocation, formalizing the needed information as residual authorization state. Exponentially many future-distinct states can share one transitive closure, with exact or tight asymptotic bounds on the state an exact monitor requires. Across four open-weight models, fixed 256-token summaries solved at most 2 of 16 paired episodes while authenticated current-query reads solved 15-16 of 16. A hard effect gate reduced eight observed unauthorized effects to zero without changing preceding attempts.

arXiv cs.CR·Sep 8, 00:05 UTC · 9d agoResearch

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Privacy-Aligned Personalized Federated Learning with Compact Adaptation and Variable-Length Gaussian Communication

Paper releases a private client context once and confines adaptation to coefficients, matching full-model differential privacy with 2.67x less uplink on CIFAR-10.

The paper addresses the dimensionality misalignment between record-level differential privacy and low-dimensional client variation in personalized federated learning by releasing a private client context once and restricting repeated adaptation to a fixed coefficient space. A variable-length quantized Gaussian mechanism lets quantization error itself serve as the required privacy perturbation. On MNIST and CIFAR-10, the design matches or outperforms full-model private adaptation across privacy budgets and client heterogeneity while cutting protected uplink 2.67x at epsilon=16 on CIFAR-10.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:48 UTC · 2d agoAI research1

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OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design

OptiPrime co-designs HE-MPC protocols with hardware acceleration to remove network communication bottlenecks in private DNN inference, beating Cheetah by up to 5.7x.

OptiPrime is a protocol-hardware co-optimization framework for private deep neural network inference based on hybrid homomorphic encryption (HE) and multi-party computation (MPC). It introduces a novel HE protocol for convolutions that reduces the number of transmitted output ciphertexts, addressing the network bottleneck that limits gains from commercial HE accelerators. A lightweight compression system reduces weight plaintext memory traffic by 10x, while a specialized dataflow maximizes on-chip reuse of intermediate ciphertexts. Experiments show up to 5.7x speedup over the Cheetah baseline on CPUs and 4.2x with an accelerator.

arXiv cs.CR·Sep 15, 09:29 UTC · 2d agoResearch

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Subgroup Membership Inference Audits of Differentially Private Synthetic Text

Audits of 32 differentially private synthetic-text releases show subgroup membership leakage is concentrated in few records and systematically underestimated by average-case attacks.

The paper defines a subgroup-targeted membership inference game in which the target pool is an explicit parameter, to audit residual leakage in differentially private synthetic text releases. The audit instantiates 32 proxies across four datasets, three generators (DP-SGD fine-tuning, API-based prompting, and activation steering), and five privacy budgets. DP substantially reduces average leakage at every budget, but remaining leakage is concentrated: roughly a tenth of records carries about 40% of it, and the noise removes more measured leakage from random records than from high-risk ones. Which records leak depends on the release mechanism, so record-level risk cannot be assessed independently of the release.

arXiv cs.CR·Sep 9, 07:56 UTC · 8d agoResearch

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Differentially Private EEG Feature Anonymization: A Privacy-Utility Case Study in Clinical Neurophysiology

Case study applies Gaussian and Laplace differential privacy to clinical EEG features, quantifying privacy-utility trade-offs across three deployment scenarios.

Researchers evaluate subject-level differential privacy for EEG-derived feature representations using Gaussian and Laplace perturbations across client-side, centralized server-side, and decentralized local training scenarios. Utility is assessed with statistical measures and a downstream machine-learning check on clinical neurophysiology data. Results show DP can be integrated into EEG workflows, but mechanism choice, privacy parameters, and sensitivity calibration strongly influence data utility, particularly on small and imbalanced clinical datasets. The study highlights the privacy-utility trade-off in protecting biomedical signals against re-identification and inference risks.

arXiv cs.CR·Sep 10, 16:31 UTC · 6d agoResearch

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SpliTEE: Improving LLM Inference on Trusted Hardware with Differentially Private GPU Outsourcing

SpliTEE splits LLM inference between Intel TDX trusted execution and untrusted GPUs, using differential privacy instead of encryption to protect intermediate representations.

SpliTEE extends split inference to LLMs, running inference partly inside an Intel TDX TEE while masking intermediate inputs sent to untrusted GPUs with differential privacy rather than encryption. The authors show a prompt-reconstruction attack recovers nearly 80% of prompts from unmasked intermediate representations, motivating the masking. A global sensitivity analysis bounds the required DP noise scale, avoiding quantization and keeping models in floating point. The implementation is nearly twice as fast as full CPU-based TDX inference and 5-15 seconds faster than encryption-based Slalom with higher accuracy, evaluated on Llama-3.2-3B and Qwen3-4B.

arXiv cs.CR·Sep 14, 04:55 UTC · 3d agoResearch

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Maverick: Private and Verifiable LLM Inference Made Practical via Matrix-Vector Multiplication Delegation

Maverick protocol delivers private and verifiable LLM inference via matrix-vector multiplication delegation, achieving up to 45x throughput gains over local inference on Qwen3-4B.

Maverick introduces an information-theoretically sound protocol for delegating matrix-vector multiplication with transparent preprocessing, efficient batch verification, and virtually no server overhead, combined with LPN-based pseudorandom masking for input privacy. It addresses privacy and correctness concerns when users delegate open-weight LLM inference to third-party providers. An end-to-end prototype evaluated on Qwen3-4B achieved throughput gains over local inference of up to 45x with precomputed privacy masks and 44x for verification-only workloads, with a CPU server using up to 128 threads.

arXiv cs.CR·Sep 9, 14:53 UTC · 7d agoResearch1

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Don't Send What You Don't Need: Question-Guided Token Pruning as a Privacy Defense for Vision-Language Models

QPriv-VL prunes privacy-sensitive visual tokens in federated/split VQA, cutting membership-inference success on VQA-RAD from 0.99 to 0.76-0.79 using ~40% of tokens.

The paper proposes QPriv-VL, a question-guided token-pruning framework for federated, split, and U-shaped split learning that suppresses privacy-sensitive visual patches before transmission. Its Dynamic Threshold Predictor combines cross-modal question relevance with frozen DINOv2-derived sensitivity to compute a per-sample pruning ratio and retention mask in one forward pass, without sensitivity labels. Evaluated on GQA, OK-VQA, VQAv2, SLAKE, VQA-RAD, and PathVQA against FSHA, FORA, iDLG, and attribute-inference membership inference attacks, it matches or beats fixed-ratio pruning. On VQA-RAD it reduces membership-inference success from 0.99 to 0.76-0.79 while preserving competitive accuracy with about 40% of the original token budget.

arXiv cs.CR·Sep 14, 14:48 UTC · 2d agoResearch

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An Open-Source End-to-End FHE Implementation for Privacy-Preserving Llama 3 8B Inference

Odin runs Llama-3-8B fully homomorphic encrypted inference on a single H100 in 366 seconds, a 4.51x speedup over THOR.

Odin is an open-source end-to-end GPU CKKS implementation for privacy-preserving Llama-3-8B inference that co-designs ciphertext packing with model execution. A feature-major cross-layer layout unifies residual connections and layer interfaces, while transient intra-operator layouts serve linear projections and attention, avoiding intermediate repacking of QK^T softmax outputs. Minimax polynomial approximation with input-range control reduces polynomial degree and multiplicative depth for nonlinear ops. With 128-token input, Odin evaluates all 32 Transformer layers on one NVIDIA H100 80 GB in 366.4 s using 58.9 GiB peak memory, versus 1651.9 s for the THOR baseline, a 4.51x speedup.

arXiv cs.CR·Sep 11, 02:51 UTC · 6d agoResearch

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A Note on Sphere Packing Bounds for Tuple Lattice Sieving

Proves upper bounds on k-irreducible unit vector set rates, yielding nearly tight asymptotics relevant to tuple lattice sieving in cryptanalysis.

The paper bounds the maximal asymptotic rate of k-irreducible sets of unit vectors via spherical code packing bounds. It shows R_k is sandwiched between (1/2 - o(1)) log2(k)/k and (1 + o(1)) log2(k)/k for large k. These almost-tight bounds inform subexponential complexity analyses of tuple lattice sieving, which underpins security estimates for lattice-based cryptography.

arXiv cs.CR·Sep 8, 03:20 UTC · 9d agoResearch

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Understanding the Privacy-Preserving Potential of HTTP/2 Against Webpage Fingerprinting

Researchers show HTTP/2 features can emulate website fingerprinting defenses like FRONT and Tamaraw with tunable privacy-overhead trade-offs.

An arXiv paper demonstrates that application-layer defenses such as HTTPOS, LLaMA, FRONT, ALPaCA, and Tamaraw can be emulated through HTTP/2 features at both the client and server side, including proactive resource suggestion, multiplexing, and flow control. The authors propose a unified evaluation blueprint that calibrates defense parameters per dataset, combines practical attacks with information-theoretic leakage estimators, and measures overheads to map each defense's privacy-overhead trade-offs.

arXiv cs.CR·Sep 4, 13:16 UTC · 12d agoResearch

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GAUGE: A Formal Framework for Measuring Cryptographic Security under Heterogeneous Adversary Cost Models

GAUGE frames cryptographic security as profiles over adversary cost models, certifying a ranking reversal between ML-KEM-512 and AES-128 from a 4–5% memory pricing shift.

GAUGE represents cryptographic security as a function over admissible adversary cost models (a security profile), proves profiles are piecewise-linear and concave, and establishes a rating trilemma when two profiles cross. A polynomial-time linear-programming procedure certifies whether the ranking of two schemes is robust, reverses under admissible models, or is genuinely incomparable. Applied to NIST post-quantum standards, the framework certifies a ML-KEM-512 versus AES-128 ranking reversal from a 4–5% shift in memory pricing and measures lattice-sieving cost drift of 9.79 bits per year over eight years. A hybrid X25519 + ML-KEM-768 handshake reduces combined-break probability twenty-fold at a 2.3 kilobyte cost.

arXiv cs.CR·Sep 15, 14:55 UTC · 1d agoResearch

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Topological Fraud Detection in Latent Transaction Spaces

Researchers present a privacy-preserving fraud detection method combining unsupervised filtering and supervised classification on anonymized transaction embeddings for low-latency triage.

The paper describes fraud detection performed entirely on topologically anonymized transaction embeddings. It iterates unsupervised filtering followed by supervised classification ('sniping') to flag suspicious activity. The goal is ultra-low-latency, privacy-preserving triage for institutions without exposing personally identifiable information.

arXiv cs.CR·Sep 8, 08:50 UTC · 9d agoResearch

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SCHERI: Provably Secure Speculation Under the Constant-Time Policy for CHERI (Extended Version)

Researchers formally prove existing CHERI speculation proposals leak secrets and present SCHERI, a processor design with end-to-end Spectre-resistant constant-time guarantees.

The paper builds a formal framework reasoning jointly about capability safety, speculative execution, and information-flow security on CHERI architectures. It demonstrates that existing secure-speculation proposals fail to preserve constant-time confidentiality guarantees and can transiently leak isolated secrets. The authors present SCHERI, a new processor design formally proven to provide end-to-end secure speculation for the constant-time policy, resilient to Spectre attacks.

arXiv cs.CR·Sep 15, 16:33 UTC · 1d agoResearch

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Cascading Gradient Inversion via LT-Code Inspired Peeling in Federated Learning

New gradient inversion attacks tied to erasure-coding theory recover 94–100% of ImageNet batches, showing federated learning privacy leakage is underestimated.

The paper connects gradient inversion in federated learning to erasure-correcting code theory, constructing analytic attacks that exceed previously known recovery bounds. The attacks recover batches exactly, with every sample's label, from a single FedSGD round, and certify each recovery without ground-truth data. On eight image and tabular benchmarks, even a passive attacker observing an honestly trained network recovers 94–100% of ImageNet batches up to size 128, and more than 90% actively at batch sizes of several hundred. The authors conclude that federated learning's privacy leakage has been underestimated.

arXiv cs.CR·Sep 9, 03:17 UTC · 8d agoResearch

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Dependency-Aware ROM/CBD Correctness Bounds for ML-KEM-768 at the Heuristic Failure Scale

Researchers certify a dependency-preserving upper bound of 2^-164.81 on honest decapsulation failure for ML-KEM-768 within an explicit ROM/CBD abstraction.

The paper models ML-KEM-768's domain-separated public-matrix streams as independent uniform ring elements and secret polynomials as CBD2 primitives, explicitly not claiming an information-theoretic result about the fixed SHAKE instantiation in FIPS 203. It preserves dependencies from the public matrix and both ciphertext-compression terms, using a graph-coupled reference, a proper-ideal bivariate Fourier transport, and a 256-coordinate union bound. The certified bound is Pr[K' != K] <= 2^-164.81, with the exponent 164.8107162... exceeding the 164.81 threshold by only about 0.0007162 bits; 164.82 is not certified. The bound applies to messages fixed independently of the randomness under honest encryption and decapsulation, and is not an exact DFR, a fixed-SHAKE equivalence, or a new IND-CCA reduction.

arXiv cs.CR·Sep 9, 10:13 UTC · 8d agoResearch1

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ASLEval: Measuring Privacy Exposure Displacement in LLM Agent Sessions

ASLEval benchmark shows local privacy proxies miss 46.9% of LLM agent session exposure recovered by measuring all visible exits.

Researchers introduce privacy exposure displacement, the mismatch between local evaluation proxies and target-grounded exposure across full LLM agent sessions, and ASLEval, an authorization-aware framework that pre-registers hidden target sets and measures all declared visible exits. Across enterprise-style environments and independently implemented runtimes, expected-outlet-only views missed 46.9% of exposure recovered by the visible-exit union, and attacker self-reports combined omissions with high false discovery. Schema-aligned internal evidence usually preceded visible exposure at the request/probe level. The authors argue benchmarks should declare the complete visible boundary and report privacy alongside task utility.

arXiv cs.CR·Sep 16, 16:03 UTC · 19h agoAI safety & security

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