Aug 2026· ICST Transactions on Scalable Information Systems· Vol 13· 0 citations· 39 references
TL;DR
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.
Abstract
To address privacy leakage from user images, intermediate representations, and outputs during Vision Transformer (ViT) inference in distributed edge services, this paper presents SViT, a two-server secret-sharing framework with offline correlated randomness. The revised design specifies fixed-point arithmetic over the ring Z_(2^64) with 16 fractional bits, fresh one-time masks, probabilistic truncation, numerical ranges, and complete input-output procedures for SExp, SDiv, SSqrt, SVar, SLayerNorm, SSoftmax, and SGeLU. The protocols use fixed-depth range reduction, lookup-assisted initialization, and a constant number of Newton updates, so their online depth is independent of numerical convergence tolerances. Under the semi-honest, non-colluding-server model, the revised security analysis defines approximate ideal functionalities, public leakage, simulator inputs, and sequential composition. Existing microbenchmarks show 2.28-6.50 times lower runtime and 4.00-14.20 times lower online communication than CrypTen for the reported core operators; for SDiv, runtime decreases from 9.1 ms to 1.4 ms and communication from 10.8337 MB to 0.7629 MB. Synthetic Q16 numerical checks report maximum absolute errors of 5.10×10−5 for SExp and 4.73×10−4 for SGeLU, maximum relative errors of 1.27×10−5 for reciprocal and 3.72×10−5 for square root, and 100% top-1 consistency over 10,000 randomly generated Softmax vectors. The reported end-to-end result remains 3799.744 ms and 2.85 GB per inference; therefore, SViT 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.
Multi-server private information retrieval (PIR) based on function secret sharing (FSS) has emerged as a prominent paradigm for achieving sublinear communication. However, standard FSS constructions require full server participation, making them highly vulnerable to single-node fail-stop faults. Existing fault-tolerant schemes mitigate this but inevitably inflate the response overhead to scale with the database size N (e.g., O(N)). To overcome this limitation, we propose a fault-tolerant PIR (FT-PIR) protocol based on a newly designed (t,p)-threshold distributed point function (FT-DPF). By introducing a hierarchical recursive patching mechanism, our scheme transforms rigid all-party evaluations into flexible t-out-of-p reconstructions. This architecture completely decouples the response communication from N and ensures efficient client-side reconstruction via lightweight XOR aggregations. Formal analysis proves that our stateless protocol guarantees (t−1)-computational privacy under the semi-honest model. Theoretical analysis demonstrates that the proposed FT-PIR achieves a response complexity bounded by O(Fmaxlevel(t,p)). Comprehensive experimental evaluations confirm that our implementation significantly reduces practical communication and computation overheads, outperforming the state-of-the-art scheme.
Dazeng Yuan, Xi-Heng Liu, Bin Liu· Entropy· 0 citations
Private inference protects both user inputs and server models during neural network inference, but existing solutions remain too slow for practical deployment. This motivates recent efforts to run a public encoder, such as a pretrained backbone, outside the protection boundary and evaluate only a small private predictor cryptographically. While appealing for efficiency, this design is not inherently secure: naively offloading a public encoder may create a feature-space shortcut: an extraction adversary may learn the remaining private predictor's feature-to-output mapping more easily than the original model's input-to-output behavior. We present Gecko, designed to limit this additional risk while retaining a compact encrypted predictor. We leverage a frozen backbone that contributes hierarchical features, fixed Fastfood projections that compress them, and private feature gating that prepares them for prediction. We formalize ideal independence and information-preservation conditions as design guidance, then separately evaluate component-reuse extraction attacks. Across image and audio tasks, Gecko achieves 0.4-2.2 second inference with at most 10.8 MB communication and accuracy comparable to transfer-learning baselines. Under the evaluated attacks, reusing the offloaded public encoder provides no significant advantage to model-extraction adversaries. Source code and a demo are available at https://github.com/CassiniHuy/gecko-infer.
Cheng'an Wei, Kai Chen, Yue Zhao et al.· 0 citations
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.
Aniket Chakraborty, S. Chakravarty· 2026 International Conferenc...· 0 citations
Secret sharing is a vital task in protecting a sensitive information over distributed systems since a portion of a secret is split into at least two or more parts so that only specific group of participants can recreate the original information. Classical schemes such as those of Shamir provide good mathematical underpinnings, but cannot support more modern security requirements such as dynamic group structures, multi-secret management and resistance to cheaters, whose features are becoming important in modern security designs. In this paper, we develop a new, effective, and scalable secret sharing algorithm which is derived using recursive symmetric bivariate polynomial equations. The scheme is proposed to facilitate dynamic (k, n) thresholds, multi-secret encoding, cheater detection and efficient memory utilization. The system guarantees robust reconstruction even in adversarial conditions, using a recursive polynomial structure and dual-level interpolation. The experimental performance proves to be highly efficient, consuming low resources, with accuracy in identifying a cheater hence this method is extremely applicable in cloud computing, secure multi-party computation, and zero-trust environment.
Poonam Kadam· Journal of Intelligent Decis...· 0 citations
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
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(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,
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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.
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is agnostic to other CKKS optimizations and can be combined with them for further gains.
Dimitrios Schoinianakis, M. Sabzevari· IACR Communications in Crypt...· 0 citations
Instance encoding is a popular empirical technique for privacy enhancement when sharing data to an untrusted server. It transforms sensitive data through an encoding process before sharing, with the hope that the encoding process retains utility but makes it hard to reconstruct the original data. However, most work offers no theoretical guarantee that the encoding process is actually irreversible. A recent work derived a mean-squared error (MSE) bound limiting any adversary's reconstruction accuracy, offering one of the first theoretical results in this domain. This bound, however, has three critical limitations: it is often too loose, only works with randomized encoders (excluding many deterministic encoders practitioners use), and only bounds MSE. We introduce a family of new bounds that (1) are tighter, (2) applicable even to fully deterministic encoders, and (3) can extend beyond MSE to other norm-based similarity metrics, by properly accounting for the encoder's spectral structure. We evaluate our bounds across a range of encoders, datasets, and attacks, showing they hold consistently and improve upon the existing bound.