MPC for Collaborative Design: Privacy-Preserving CAD, DFM, Compliance, and Multi-Party Optimization

Introduction

Minimal framing

Design organizations want the speed of cross-company collaboration without relinquishing intellectual property. The obstacle is not desire but exposure: sharing native CAD, manufacturing features, or cost models leaks far more than a specific answer. **Secure Multi-Party Computation (MPC)** changes the game by letting teams compute on private data and reveal only authorized outputs, such as a clash boolean, a manufacturability score, or a clamped KPI—never the underlying geometry or rules. In this article, we map the cryptography to concrete design tasks and provide an implementation blueprint that product developers, manufacturing engineers, and PLM owners can use today. Our focus is the “how”: which primitives fit which engineering kernels, how to encode geometry robustly, when to combine MPC with **Trusted Execution Environments (TEEs)** or **differential privacy**, and what governance is necessary to satisfy export control, NDAs, and antitrust constraints. The result is a pragmatic path to **privacy-preserving collaboration** that scales from secure clash detection to multi-party optimization loops. The intention is minimal preamble and maximal specificity, so that teams can build pilots that respect IP boundaries while delivering immediate value. When done correctly, MPC becomes not a research curiosity but a dependable infrastructure tier sitting next to CAD, PLM, and simulation, enabling cross-company design work that used to be legally, competitively, and culturally impossible.

Why Secure Multi-Party Computation for Collaborative Design

Problem scenarios

Real-world collaboration in product and architectural design is riddled with hidden constraints and proprietary know-how. MPC provides a safe conduit for the answers that matter while keeping everything else private. Consider these high-value scenarios where **computations must cross company boundaries** without exposing design intent:

• Cross-company interference checks without disclosing geometry: Parties submit secret-shared bounding volume hierarchies and run secure AABB and triangle-triangle tests. The only reveal is a clash flag plus minimal witness data (e.g., a hashed pair ID).
• Supplier manufacturability/costing on hidden features: OEMs share derived features (wall thickness fields, hole patterns) as secret shares; suppliers evaluate proprietary DFM rules and cost regressors under MPC, returning a scalar score or price range.
• Standards/compliance verification (GD&T, safety) without revealing design intent: A standards body (or internal compliance team) evaluates tolerances, clearances, and keep-outs as secure predicates, revealing only pass/fail and localized hints.
• Consortium optimization across confidential sub-systems: Multiple suppliers optimize weight, cost, and CO2 under a shared objective where sub-models remain private, revealing only aggregate progress or Pareto-improving moves.

Each scenario has an asymmetric information problem: one party needs the computation; another has the data or the rules; neither wants to reveal more than a tightly scoped outcome. **MPC narrows disclosure to the result**, codifying “need-to-know” at the level of bits rather than trust agreements.

Threat model and trust assumptions

Engineering collaboration spans distinct trust zones, so the threat model must be explicit. The baseline is **semi-honest (honest-but-curious)** adversaries: parties follow the protocol but try to learn from transcripts and outputs. Many industries require stepping up to **malicious security**, where parties may deviate or collude up to t < n. Output leakage risks loom even when the protocol is sound: a stream of carefully crafted queries can infer geometry or rules, and network or timing signals can become side channels.

• Adversary model: Define whether parties are semi-honest or malicious; set collusion bounds (e.g., tolerate up to one colluding supplier in a three-party setup).
• Output controls: Clamp return types to booleans or coarse scalars; aggregate across cohorts; add calibrated noise for analytics; throttle queries and enforce budgets.
• Protocol safeguards: Use SPDZ-style MAC checks, cut-and-choose for garbled circuits, and batched consistency checks; require transcript-signed “cryptographic receipts.”
• Side-channel hygiene: Normalize round trips, pad message sizes, and avoid data-dependent branches via circuit co-design; log and audit reveal events.

By matching **security level to risk**, and by engineering the reveal surface, you can reap collaboration value while maintaining provable privacy boundaries.

Alternatives and when to use them

MPC is powerful, but it’s not the only tool. The right choice depends on latency targets, threat model, and regulatory constraints. In many pipelines, a hybrid approach is the sweet spot.

• TEEs (e.g., SGX, SEV): Great for single-tenant execution with low latency and complex control flow. Trust anchors include CPU vendor and attestation. Use TEEs for coarse culling or pre-filtering; fall back to MPC where multi-party trust is required.
• MPC: Best when no single party should see plaintext inputs. Ideal for pairwise or multi-party checks (interference, DFM, compliance) with predictable circuits.
• Federated analytics: Suitable for training shared models without sharing raw data; combine with MPC for secure aggregation, or with differential privacy for generalization guarantees.
• Differential privacy: Apply to aggregated KPIs or dashboards; avoid for per-instance decisions where noise would invalidate acceptance criteria.
• Zero-knowledge proofs (ZK): Provide attestations (“clearance ≥ x,” “hole pattern conforms to spec”) where verifying parties need trust without rerunning heavy computation.

A pragmatic pattern is a **hybrid pruning pipeline**: TEE executes high-branching, coarse geometry filters; survivors are handed to MPC for exact, privacy-preserving checks. For reporting, layer **differential privacy** to safely publish aggregated manufacturing KPIs.

IP boundaries and legal/compliance drivers

The primary value of MPC is not just technical—it’s organizational compliance. **Export control**, supplier NDAs, antitrust “no-peek” procurement, and **data residency** are powerful constraints that MPC can encode and prove.

• Export control: Keep controlled geometry encrypted and processed only on compliant infrastructure; audit keys and parties; log proof that no plaintext ever crossed borders.
• Supplier NDAs: Replace vague “limited use” clauses with **cryptographically enforced reveal policies**: suppliers get scores or pass/fail, not your feature tree.
• Antitrust/no-peek procurement: Evaluate bids using secure computation where buyer rules and supplier costs remain secret; reveal only rankings or allocations.
• Data residency: Pin MPC parties and keys to specific regions; store audit trails of jurisdictional compliance and offer **crypto agility** for evolving policy regimes.

By aligning MPC’s **reveal-minimizing guarantees** with legal obligations, organizations can unlock cooperation that previously stalled in legal review, while preserving a verifiable chain of custody for every revealed bit.

Mapping Cryptographic Primitives to Design Tasks

Core MPC and crypto building blocks

Selecting the right primitive per workload is the difference between a proof-of-concept and a production pipeline. For arithmetic-heavy kernels—mass properties, regression inference—the workhorses are **additive or replicated secret sharing** protocols (e.g., SPDZ, ABY3, MP-SPDZ implementations). For control-heavy checks—feature presence, rule trees—**garbled circuits** and **Oblivious Transfer (OT)** excel. When the math is linear or near-linear, **homomorphic encryption** (CKKS/BFV) can shine, especially for single-party compute with encrypted inputs. Catalog reconciliation requires **Private Set Operations** (PSI/PSU), and **Zero-Knowledge proofs** shine when third parties must verify a property without re-running the computation.

• Secret sharing (SPDZ/ABY3): High-throughput, vectorized arithmetic; ideal for geometry algebra, inertia tensors, tolerance stacks, and gradient steps.
• Garbled circuits + OT: Branchy logic for feature tests, rules, table lookups, and discrete decisions; combine with secret sharing via hybrid protocols.
• HE (CKKS/BFV): Encrypted linear scores or polynomial approximations; pack vectors to evaluate many parts simultaneously.
• PSI/PSU: Bill of Materials (BOM) reconciliation, common vendor library matching, and IP-safe catalog joins.
• ZK proofs: Attest “meets spec” without revealing how; staple for compliance and **property attestation**.

Think of these as a palette: blend secret sharing for numerics, circuits for control flow, HE for linear analytics, PSI for set relationships, and ZK for verifiable disclosures.

Geometry and manufacturing predicates as secure circuits

Geometry is computable under MPC if you express it as a stack of robust predicates. Start with broad culling, then refine to exact checks, mirroring conventional geometry pipelines but in a constant-time, branch-regularized form. For **interference**, use secure BVH pruning and **AABB** tests over hashed cells to limit candidate pairs. Surviving pairs go to exact triangle-triangle intersection under secret sharing. For envelopes and keep-outs, encode **point-in-polyhedron**, signed distance, and nearest neighbor queries using fixed-point arithmetic. Manufacturing predicates—wall thickness, minimum radius, overhang angle, tool reachability—become circuits that operate on secret-shared fields or medial-axis approximations.

• Collision/interference: Secure BVH + AABB → triangle-triangle exact checks; reveal only “clash?” and optional hashed witness IDs.
• Envelope checks: Point-in-polyhedron and signed distance for keep-out zones; return booleans or clamped minimum margin.
• Tolerances/mass properties: Compute centroid, volume, inertia tensors from tessellations; aggregate tolerance stacks via linearized models.
• DFM scores: Evaluate wall thickness via distance transforms; detect undercuts or small radii; compute a composite score.
• Cost/lead time: Secure inference with linear or nonlinear regressors on private feature vectors; optionally HE for single-party scoring.

The trick is to keep kernels **branch-free and numerically stable** under fixed-point, so that circuit size is predictable and leakage via timing is minimized.

Numeric representation and robustness

Robustness is paramount: geometry kernels fail noisily when numeric decisions are brittle. Under MPC, you can’t peek and patch; you must encode resilience. Start by normalizing units and bounding ranges; choose fixed-point scales to preserve engineering tolerances while containing circuit depth. Guard against overflow with modular checks, and consider **range proofs** to ensure inputs fall within validated bounds. Where exactness matters (orientation tests, intersection), prefer **exact predicates via modular arithmetic** or emulate interval arithmetic to capture numerical uncertainty. Quantization reduces circuit size; the art is choosing step sizes that do not erase functional tolerances.

• Fixed-point scaling: Normalize to meters or millimeters; select scale (e.g., 1e-4) to reflect tolerance budgets.
• Overflow guards: Pre-check magnitudes; use limb-wise checks or modulus-aware comparisons with error flags.
• Exactness where needed: Use modular orientation tests; conservative interval bounds for “definitely collide” vs “uncertain → refine.”
• Quantization strategies: Coarse for pruning, fine for exact checks; record scale in reveal receipts for auditability.
• Range proofs: ZK assertions that coordinates/features are within certified ranges without revealing their values.

By engineering numerics first, you build circuits that are trustworthy at design tolerances and efficient enough to meet latency budgets.

Optimization patterns

Optimization under privacy involves iteration without leakage. For multi-party objectives—weight, cost, CO2—you can run **secure gradient steps** where each party contributes partial derivatives under secret sharing. A **secure line search** reveals only policy-approved scalars (e.g., step accepted yes/no, or clamped objective improvement). Reveal policies dictate what is learned per iteration to prevent reverse engineering of private models. Where gradients are too costly or non-smooth, adopt **surrogate modeling**: parties train local models and share parameters via secure aggregation (federated + MPC), producing a joint predictor that no single party can attribute to a competitor’s data.

• Secure gradient descent: Accumulate gradients in MPC; update parameters with fixed-point arithmetic; reveal minimal progress metrics.
• Line search/reveal: Compare objective deltas under MPC; disclose only accept/reject and bounded improvement.
• Surrogates: Securely aggregate model weights; optionally enforce differential privacy for generalization and leakage control.
• Stopping rules: Terminate by consensus thresholds; expose only aggregate convergence signals, not per-party residuals.

These patterns enable **multi-party optimization** loops that respect IP and provide just enough visibility to steer design decisions.

Reference Architecture and Implementation Blueprint

System components

A production deployment needs more than crypto; it needs connectors, orchestration, and governance. Start with CAD/PLM connectors that export **STEP/Parasolid/USD** selectively, with feature redaction and unit normalization. An **MPC orchestration service** manages session setup, party enrollment, circuit selection, and reveal policies. To cut latency, add a **precomputation service** that continuously generates Beaver triples and OT correlations. Wrap identities in **PKI**, use threshold keys where possible, and store roots in HSM-backed vaults. Finally, a policy engine and audit layer govern “who sees what, when,” issuing signed receipts for inputs, computations, and reveals.

• CAD/PLM connectors: Export tessellations, derived fields, or hashed identifiers; apply redaction templates.
• MPC orchestrator: Handles circuits, parameters, and session lifecycles; enforces **typed reveal contracts**.
• Precompute tier: Scales triple/OT generation to meet burst demand; caches correlations near participants.
• Keys and identity: Threshold decryption, short-lived session keys, mutual TLS, and attestation where TEEs are used.
• Policy/audit: Machine-readable policies, export-control flags, residency tags, and immutable logs.

Together, these components turn cryptographic protocols into a dependable, auditable service within the design toolchain.

Workflow patterns

Successful teams follow rhythms that balance privacy, latency, and cost. Use a **two-phase execution** model: heavy precompute offline, then fast online interactions for queries. Start with coarse, low-cost secure tests; only refine disputed regions. Keep results lean: scalars and booleans beat full traces, and clamped ranges prevent undue inference. For design exploration, use **incremental recompute**—cache secret-shared states and recompute only differences for param sweeps and design-of-experiments.

• Offline precompute: Generate triples/OT, prehash spatial cells, and stage PSI tokens before meetings.
• Online interaction: Aim for sub-500 ms for interactive checks by limiting circuit depth and batching micro-queries.
• Progressive disclosure: AABB first; triangle checks only on candidates; reveal minimal witnesses.
• Incrementalism: Reuse shares; keep persistent sessions across design revisions with change-only updates.
• Reveal minimality: Prefer booleans and bounded scalars; consider differential privacy for dashboards.

This structure lets collaborators iterate quickly while maintaining **privacy budgets** and predictable compute costs.

Performance engineering

Performance comes from **circuit co-design** and network-aware execution. Rewrite kernels to be branch-regular, vectorize arithmetic, and pack workloads (e.g., HE ciphertext packing for batch scoring). Use secure geometric pruning via hashed spatial cells or BVHs to reduce candidate checks by orders of magnitude. Place parties strategically to minimize inter-region hops; scale triple generation to match peak bandwidth. Set clear latency targets: **interactive checks < 500 ms** with batched, shallow circuits; batch compliance and optimization can run in minutes with aggressive parallelism.

• Kernel design: Strip data-dependent branches; table-ize constants; saturate SIMD-friendly MPC backends.
• Pruning: Securely cull with coarse spatial hashing; escalate precision only as needed.
• Network: Co-locate precompute with parties; use QUIC/TLS; monitor round-trip variance.
• Targeting: Reserve deep circuits for batch; keep UI-driven checks shallow and pre-batched.

Treat performance as a first-class design goal, just like robustness and security.

Security and governance

Security choices are context-driven. **Semi-honest** protocols are fast and adequate for many supplier interactions, particularly with strict reveal policies. Where stakes are high, add **malicious security**: SPDZ MAC checks, cut-and-choose for garbled circuits, and authenticated channels. Configure collusion resilience via party count and quorum rules. Guard against output leakage with result throttling, counterfactual query checks, and per-party **query budgets**. For compliance, maintain export-control logs, pin data residency, and rotate keys under a crypto-agile policy.

• Security flavors: Semi-honest for speed; malicious for high-assurance; document the choice per workflow.
• Collusion bounds: Align quorum with risk (e.g., 3 or 5 parties) and document t < n assumptions.
• Leakage controls: Rate limits, clamp outputs, and block correlated queries designed to infer private values.
• Compliance: Residency pinning, export tags, auditable receipts for every reveal, and **key rotation** SLAs.

Security is not just math; it is policy, monitoring, and proof that policies were enforced.

Tooling and standards

Adopt mature protocol stacks and codify interfaces so MPC becomes a reusable service. For secret sharing and hybrid protocols, **MP-SPDZ**, **EMP-toolkit**, **ABY/ABY3**, and **SCALE-MAMBA** provide robust baselines. For HE, **SEAL** and **PALISADE** are strong choices. Build a **secure geometry kernel library** with prebuilt predicates (AABB, triangle intersection, point-in-polyhedron), manufacturability circuits, and PSI adapters for catalogs. Expose **API patterns**: headless CAD endpoints, job descriptors, and typed reveal contracts that specify allowed outputs and precision.

• Protocol stacks: Prefer actively maintained repos with malicious security options and vectorized ops.
• HE libraries: Choose CKKS for approximate analytics, BFV for exact integer arithmetic.
• Geometry kernel: Package predicates as reusable circuits; validate numeric ranges and scales.
• APIs: Define schemas for inputs, circuits, and reveals; version policies and deprecation windows.

Standardizing these pieces elevates MPC from bespoke projects to a platform capability inside the design ecosystem.

Conclusion

From possibility to practice

Privacy-preserving computation is now practical for the design stack. With **MPC**, organizations can run interference checks, manufacturability scoring, costing, and compliance verification without exposing models or rules. This shifts collaboration from manually sanitized exports and brittle NDAs to **cryptographically enforced reveal policies**. The immediate impact is better supplier engagement, faster design iteration, and fewer legal hurdles. Longer term, multi-party optimization loops will let consortia improve weight, cost, and CO2 collectively while safeguarding IP. ZK attestations will allow regulators and customers to verify conformance without peering into proprietary geometry. The crucial insight is that privacy and productivity are not in tension when the architecture blends the right primitives, circuit-aware geometry kernels, and disciplined governance. Rather than isolating partners behind opaque PDFs and over-the-wall handoffs, teams can compute together and reveal just enough to act, creating a flywheel of trust across the supply chain. That is the practical promise of **privacy-preserving collaboration** in engineering.

Pragmatic roadmap

Adoption works best when sequenced. Start with short-term wins that are easy to explain and govern; layer in richer capabilities as confidence grows. Priorities that consistently deliver value include:

• Secure clash detection: AABB → triangle checks with boolean reveals; integrate with viewer flags, not geometry.
• PSI-based BOM alignment: Privately match part numbers and vendor libraries; escalate only mismatches for remediation.
• Private manufacturability scoring: Supplier rules under MPC return a scalar score and limited hints; keep features secret.

As teams mature, expand to **multi-party optimization** with secure gradient steps and policy-bound reveals, and introduce **ZK attestations** for compliance claims. Next steps to institutionalize the capability:

• Standardize secure predicate libraries for CAD: Package and certify AABB, triangle intersection, and DFM predicates with fixed-point scales.
• Define reveal policy schemas: Machine-readable, versioned, and auditable contracts governing every output bit.
• Integrate MPC orchestration into PLM: Treat sessions, parties, and receipts as first-class PLM objects tied to change orders.

By following this roadmap—grounded in crisp use cases, production-grade tooling, and strong governance—organizations can make **privacy-preserving collaboration** routine, turning secure computation into a competitive advantage that compounds with every project.