Privacy-Preserving AI Inference

• Privacy-preserving AI inference is a set of cryptographic, architectural, and algorithmic methods that enable secure machine learning predictions on encrypted sensitive data.
• Techniques like homomorphic encryption, SMPC, and trusted execution environments protect data privacy while supporting applications in healthcare, cloud MLaaS, and edge AI.
• Recent innovations include joint encoding strategies, differential privacy adaptations, and verifiable inference methods that optimize latency and balance privacy with utility.

Privacy-preserving AI inference encompasses a range of cryptographic, architectural, and algorithmic methods designed to enable machine learning model prediction on sensitive input data while ensuring that neither the input data nor the (often proprietary) model parameters are exposed to untrusted computation environments. These techniques have become central to AI deployment in regulated industries and cloud-based Machine Learning as a Service (MLaaS) due to the confluence of data privacy legislation, threat of information leakage, and the ubiquity of AI-enabled services.

1. Foundational Cryptographic Techniques

A diverse set of cryptographic primitives underpins modern privacy-preserving AI inference:

• Homomorphic Encryption (HE): Enables computation (linear and, in some cases, low-degree polynomial) over encrypted data. Early systems such as CryptoNets leveraged HE's SIMD capabilities, but suffered from substantial latency, memory, and network width limitations due to per-element encryption. Subsequent work introduced the LoLa method, optimizing data representation by switching dynamically between dense, sparse, stacked, and convolution-specific encodings to efficiently aggregate homomorphic operations. This reduces the number of costly ciphertext rotations and drastically improves practical latency while maintaining the same security level (Brutzkus et al., 2018).
• Additively Homomorphic Encryption (AHE): Used for protocols where only additions and scalar multiplications suffice (e.g., private linear prediction, logistic regression, and the first layers in simple neural networks). Efficient schemes such as Paillier allow for “request–response” protocols that require only a single round trip between client and server (Joye et al., 2019).
• Secure Multi-Party Computation (SMPC): Techniques such as Yao’s Garbled Circuits (GC) combine with oblivious transfer networks and log-domain arithmetic for the secure, exact evaluation of Sum-Product Networks (SPNs) (Treiber et al., 2020). SMPC is also used in federated learning settings to aggregate model updates securely (Ziller et al., 2020).
• Trusted Execution Environments (TEEs): Intel SGX enclaves host the most sensitive computations, often interleaved with accelerator-based execution (e.g., GPU). In frameworks such as Origami, cryptographic blinding is performed inside the enclave for initial DNN layers, after which computation switches to accelerators based on empirical feature-leakage analysis (Narra et al., 2019).

2. Architectural and Representation Innovations

Privacy-preserving inference systems demonstrate substantial advances in how data and model representations are handled to minimize both computational and information-theoretic costs:

Representation Strategy	Core Mechanism	Effect
Dense/Stacked/Convolutional	Encode entire vectors/multiple features jointly	Reduces number of rotations/messages
Transfer Learning	Local feature extraction before encryption	Limits HE ops to shallow classifier
Quantized / Integer Networks	Layer-wise bitwidth scaling and binarization	Converts FP ops to cheaper integer ops
Latent Space Projection	Adversarially obfuscated autoencoder bottleneck	Disentangles/erases sensitive features

Earlier HE-based methods (per-element SIMD) struggled with both latency and scalability. By switching to joint encoding strategies, LoLa reduced prediction time on MNIST from 205 seconds to 2.2 seconds (Brutzkus et al., 2018). Transfer learning-based confidential inference preprocesses data via public, robust feature extractors (e.g., deep ResNets), encrypts low-dimensional semantic representations, and only applies the privacy-preserving protocol to the shallow “head,” maintaining both accuracy and practical responsiveness.

For transformers, THE-X (Chen et al., 2022) replaces non-HE-supported functions (GELU, softmax, LN) with piecewise polynomial or distillation-based approximations amenable to encrypted computation, incurring <1.5% accuracy drop across benchmark tasks.

3. Differential Privacy and Feature-Space Perturbation

Differential privacy (DP) mechanisms are widely adapted from their classical dataset-level form to feature-level and token-level guarantees:

• Feature Differential Privacy: Edge sensing pipelines for collaborative inference clip feature vectors, apply orthogonal or random projection for dimensionality reduction, and inject Gaussian noise calibrated to the sensitivity (determined by the norm of the clipped features and the projection matrix’s spectral norm) (Seif et al., 23 Oct 2025, Seif et al., 25 Oct 2024, Seif et al., 1 Jun 2024). The resulting “feature DP” provides a formal (ε, δ) bound on information leakage from each transmitted feature, directly analogous to classical DP but targeting the feature-extraction stage rather than dataset queries.
• Differential Privacy in Textual Prompts: For black-box LLMs, frameworks like InferDPT (Tong et al., 2023) employ local DP-based perturbation, sampling replacement tokens for privacy-sensitive document parts via the exponential mechanism, with semantic scoring derived from embedding proximity. The RANTEXT mechanism builds “random adjacency” lists to defend against adaptive embedding-reconstruction attacks by ensuring that any token in the vocabulary could plausibly be chosen.
• Adversarial Latent Obfuscation: Latent Space Projection (LSP) (Krishnamoorthy, 22 Oct 2024) employs autoencoders with partitioned latent spaces and adversarial training (via a privacy discriminator) to ensure sensitive attributes cannot be easily inferred from the representations passed to downstream inference. This enables a tunable privacy-utility balance while supporting regulatory mandates for pseudonymization.

4. Application Ecosystems and Practical Deployment

Privacy-preserving inference methods are deployed in a variety of real-world settings:

• Medical Imaging: PriMIA (Ziller et al., 2020) utilizes federated learning with secure aggregation (via SMPC) for training on distributed, sensitive data and supports end-to-end-encrypted inference using secret sharing and function secret sharing. Empirically, such models outperform human experts on pediatric chest radiography triage and withstand advanced gradient inversion attacks.
• Cloud MLaaS and Edge AI: Efficient XGBoost prediction (via OPE + AHE) supports querying commercial decision-tree ensembles without revealing either queries or model internals, as validated on AWS SageMaker deployments (Meng et al., 2020). Edge devices in collaborative wireless environments exploit over-the-air aggregation with feature DP to reduce communication overhead and latency for use cases in autonomous driving, biometrics, and smart surveillance (Seif et al., 25 Oct 2024, Seif et al., 1 Jun 2024).
• Quantized Transformers: Secure inference on quantized BERT models leverages dual-secret-sharing, layer-wise scaling, and two-stage lookup table protocols for softmax and other nonlinearities, achieving up to 22× speedup over prior MPC-based approaches and supporting practical inference latencies in cloud healthcare (Lu et al., 3 Aug 2025).
• Agentic Architectures: Agentic-PPML (Zhang et al., 30 Jul 2025) proposes a role separation where LLM orchestrators parse plaintext task intent, but all cryptographically-secure computation—including encrypted model evaluation and secure activation—occurs in domain-specific MCP servers, dramatically reducing cryptographic workload for typical LLM queries.

5. Attacks, Defenses, and Theoretical Criteria

Privacy-preserving inference research is driven by advances in both attack methodologies and principled defenses:

• Model Inversion Attacks (MIAs): MIAs aim to reconstruct input data from intermediate features. Theoretical analysis using information-theoretic constructs (mutual information, entropy, effective information volume δ(z)) establishes a lower bound on attack difficulty. SiftFunnel (Liu et al., 1 Jan 2025) uses this framework to design edge models with reduced mutual and effective information transmission, integrating funnel-shaped architectures and correlation-based loss constraints to boost reconstruction error by ~30% and reduce edge memory nearly 20× without significant accuracy loss.
• Gradient Leakage and Membership Inference: Federated and distributed settings are threatened by gradient-based inversion and membership inference attacks. Secure aggregation via SMPC, label smoothing, and adversarial bottleneck architectures empirically mitigate these risks (Ziller et al., 2020).
• Verifiable Inference: In MLaaS, privacy-preserving computation is insufficient without integrity guarantees. vPIN (Riasi et al., 12 Nov 2024) combines partial HE (for CNN-layer computation) with commit-and-prove succinct non-interactive argument of knowledge (CP-SNARK) protocols, delivering proofs that the correct model was applied to encrypted patient data, with proving times orders-of-magnitude below naive encoded circuits.

6. Limitations, Trade-offs, and Future Perspectives

Trade-offs are inherent:

Method/family	Privacy Guarantee	Main Cost/Constraint	Typical Latency
Full HE on deep networks	Strong, theoretical	High memory/computation	Hundreds of sec (old)
LoLa / Dense rep. + Transfer	Strong, practical (HE)	Client-side preprocessing	~0.16 to 2.2 sec
SMPC/GC (SPN inference)	Provable, semi-honest	Circuit size/setup latency	Secs (medium models)
Feature/Token DP	Formal (ε, δ)-DP	Utility loss/noise addition	Tunable
Verifiable inference (vPIN)	Privacy + integrity	Proof generation overhead	Minutes

• Utility–Privacy–Latency trade-off: Feature-level DP, clipping and quantization, and architectural funneling can all reduce privacy risk, but excessive perturbation or compression degrades inference accuracy.
• Scalability: Homomorphic encryption schemes, while secure, must manage message noise growth and ciphertext bloat as models scale. Layer-wise quantization and hybrid secret-sharing help, but the challenge remains for frontier LLMs.
• Deployment complexity: Some techniques, such as transfer learning for encrypted inference or two-phase computation split between TEEs and accelerators, necessitate careful orchestration of trusted resources and precise empirical risk analysis (e.g., using c-GANs for privacy evaluation).
• Regulatory alignment: Modern systems (e.g., LSP (Krishnamoorthy, 22 Oct 2024)) are designed to interface with GDPR, HIPAA, and CCPA requirements for data minimization, consent, and auditability, complementing technical protection with governance support.
• Research directions: Ongoing efforts focus on refined theoretical privacy guarantees, optimal composition of cryptographic primitives for neural architectures (e.g., B-splines in KANs (Lai et al., 12 Sep 2024)), communication-efficient protocols for interactive MLaaS, and integrating privacy mechanisms within federated and agentic AI ecosystems.

Privacy-preserving AI inference continues to evolve, with state-of-the-art systems integrating optimized representation, rigorous DP at the feature and token level, cryptographically backed secure computation, and architectural support for real-world deployment in regulated, adversarial, and resource-constrained environments.