Spectrum-Aware Bounds on Invertibility for Privacy-Enhancing Instance Encoding

Compressed FHE

Privacy-preserving machine learning and encrypted statistics increasingly require evaluating long chains of matrix products directly on ciphertexts. In the CKKS homomorphic encryption scheme, however, every multiplication amplifies noise and enlarges ciphertexts, so the available precision budget is exhausted after only a few products. This work establishes c F H E (compressed FHE), a unified analytical and empirical framework that integrates low-rank matrix factorization techniques into the CKKS homomorphic encryption scheme. Its central idea is a co-design: instead of tuning the low-rank approximation and the cryptographic parameters in isolation, c F H E balances the two error sources against each other so that neither wastes precision. Theoretical bounds are derived for the accumulation of relative error across sequences of factorized matrices, leading to an explicit expression for the attainable computation depth as a function of target accuracy, norm amplification behavior, and per-layer approximation quality. Extensions to tree-based evaluation structures are also formulated, allowing depth to scale logarithmically with the number of factors. Concretely, this co-design is realized as a precision-balancing model that, for a target accuracy, automatically selects the CKKS parameters: the polynomial modulus degree, the modulus chain, and the scaling factor. Experimental evaluations demonstrate that encrypted low-rank matrix multiplications achieve both significant runtime improvements and reduction of ciphertext sizes over direct or tree-based encrypted multiplications while maintaining the prescribed accuracy. c F H E is agnostic to other CKKS optimizations and can be combined with them for further gains.

A Communication-Reduced Privacy-Preserving ViT Inference Framework for Distributed Edge Intelligence

SVIiT is described as communication-reduced relative to the evaluated MPC baselines rather than universally lightweight, and its present practical scope is primarily high-bandwidth LAN or provider-edge deployments.

Joint Sensing-Security Optimization in Sensing-Integrated MLWE Under Noisy and Adversarial Environments

Module Learning with Errors (MLWE) cryptography normally treats decryption noise as a disturbance: it must be large enough to hide algebraic structure but small enough for reliable decoding. This paper studies a conditional design in which selected residual degrees of freedom also carry coarse physical sensing information. The construction is not presented as a dropin replacement for standardised ML-KEM. Instead, we specify the assumptions under which residual-layer sensing can be analysed, identify what must remain external to FIPS-approved ML-KEM, and give a Fujisaki-Okamoto (FO)/CCA compatibility roadmap. The paper contributes an explicit measurement-to-label pipeline $\boldsymbol{z}=Q(F(y))$, concrete ML-KEM parameter instantiations, residual-budget calculations including compression noise, formal bounds for security-decomposition terms, higher-order leakage bounds beyond balanced mean suppression, an adaptive tuning algorithm, and residual-level Monte Carlo validation. The central message is that structured residual information can be useful for cyber-physical trust only when distributionshape closeness, decoding reliability, sensing privacy, and contextspoofing resistance are all quantified.

Cortado: Progressive Retrieval of Lossily Compressed Data with Guaranteed Error Bounds

SSTQ:Privacy-Preserving Vector Quantization via Subsampled Stochastic TurboQuant

Achieving local differential privacy in distributed optimization while maintaining low communication cost remains challenging. Existing vector quantization methods, such as vqSGD, use high-dimensional geometric constructions but incur unfavorable dimension-dependent variance. In this work, we propose Subsampled Stochastic TurboQuant (SSTQ), a framework that combines overcomplete equal-norm tight frames, coordinate subsampling, and privacy-aware one-dimensional quantization. SSTQ includes two variants: a Flat Randomized Response version and a Metric-Aware Laplace version, the latter being better suited to higher codebook bit-width regimes. We show that SSTQ achieves optimal mean squared error scaling while using only $\lceil \log_2 N \rceil + b$ bits per client, where $N = \Theta(d)$ is the frame size. We also derive a surrogate privacy-aware codebook objective that reduces the codebook-dependent MSE scaling from $O(4^b)$ to $O(2^b)$. Finally, we empirically evaluate SSTQ against established baselines on federated learning tasks using CIFAR-10 and Fashion-MNIST, demonstrating favorable utility and communication efficiency. Some of the analytical derivations were first obtained using a fully automated Gemini-based agentic system developed internally at Google. The authors have verified those derivations and edited them for clarity of presentation.

Game of Coding under Computation-Dependent Adversarial Noise

The game of coding framework was introduced to extend coding-theoretic recovery beyond its traditional limit, under which the number of honest reports must exceed the number of adversarial or corrupted reports. It does so by exploiting the rational behavior of adversarial participants and their incentive to keep the system live. Existing game-of-coding formulations, however, assume that the adversarial-noise distribution is independent of the realized ground-truth computation. This assumption may be restrictive when an informed adversary can adapt its reports to the value being computed. In this paper, we study the game of coding with input-dependent adversarial noise. We introduce a unified multi-node, multidimensional formulation. For every family of conditional adversarial-noise distributions, we construct an input-independent joint noise distribution, and prove that this reduction exactly preserves the probability of acceptance and the accepted mean-squared estimation error. Consequently, the input-dependent and input-independent models have identical achievable performance regions, and the same equilibrium utilities.