Privacy Preserving Cryptography Questions

Techniques that combine cryptography and privacy engineering to enable secure computation and data protection. Core topics include homomorphic encryption for computing over encrypted data, secure multi party computation for collaborative computation without revealing inputs, differential privacy methods for statistical analysis with privacy guarantees, oblivious transfer and related secure protocol primitives, and zero knowledge proof systems for proving statements without revealing secrets. Coverage includes practical use cases, performance and scalability limitations, parameter and threat model selection, trade offs between privacy and utility, deployment challenges, and when to prefer one approach over another.

EasyTechnical

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Explain differential privacy in the context of machine learning. Define (ε, δ)-differential privacy formally, describe the intuition behind the guarantee, and show a short example (a counting or mean query) illustrating how the Laplace or Gaussian mechanism achieves (ε, δ)-DP. In your answer, mention how sensitivity is computed for counts and means and discuss the trade-off between smaller ε and model utility.

MediumSystem Design

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Design a monitoring and governance system to track privacy-budget (ε, δ) consumption across multiple ML services and teams in production. Include APIs or SDK calls for services to log privacy-consuming operations, a central privacy ledger, dashboards, alert rules for budget exhaustion, enforcement mechanisms (rate-limiting or automatic throttling), and audit trails for compliance. Discuss trust boundaries and how to prevent teams from bypassing the ledger.

HardSystem Design

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Design a rolling-upgrade and canary deployment strategy for a model-serving stack that uses MPC/HE to compute private inferences. Requirements: zero downtime, preserve privacy guarantees during rollout, ability to A/B test new cryptographic parameters (e.g., moduli, polynomial degree), and automatic rollback on correctness or privacy regressions. Describe test harnesses, metrics and gates, and how to ensure canaries don't leak extra information.

HardTechnical

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Propose a high-level zero-knowledge proof design that proves a model update was generated from training on a dataset processed under a documented DP procedure (subsampling, clipping, DP-SGD) without revealing the dataset or model weights. Specify the statements to prove, how to handle randomized elements (noise), and practical challenges such as proving floating-point operations and large circuits.

HardTechnical

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You're optimizing HE-based inference for a relatively large model. Provide a concrete optimization plan: how to use ciphertext packing (slots) efficiently, choose CKKS polynomial modulus degree and coefficient modulus to balance security and noise budget, approximate activation functions with low-degree polynomials, apply quantization and pruning, and leverage hardware (SIMD/GPU) or multi-threading. Give rough order-of-magnitude expectations for latency improvements from each technique.

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