From the Campus Operations edition of September 21, 2026
ASU–SDSU–Utah win $5M NSF award to build PARAPET privacy-enhancing cyberinfrastructure
Arizona State University with San Diego State University and University of Utah · Higher education research computing / privacy cyberinfrastructure · United States (multi-university NSF cyberinfrastructure)
- Publisher
- Arizona State University News
- Original publication
- September 18, 2026 (ASU News)
- Source retrieved
- 2026-09-22
- Event date
- 2026-09-18
What happened
ASU News reports a $5M NSF Office of Advanced Cyberinfrastructure award (#2537035) for PARAPET (Prototype Architecture for Research Advances using Privacy Enhancing Technologies), a five-year national community resource led with SDSU and Utah. The project targets secure computation on sensitive data using differential privacy, homomorphic encryption, federated learning, and hardware that keeps data encrypted during processing; ASU’s Research Technology Office will align the prototype with institutional policies and regulated-research communities. Why now: dated September 18, 2026—after the stream’s last published edition (2026-09-19 coverage window starts from prior run)—and not previously archived.
Why it matters
Campus-operations relevance via regulated-data sharing, AI-model training without raw-data centralization, and institutional policy/compliance leadership for privacy-enhancing cyberinfrastructure that IT/research-computing and CISO organizations must operate.
Evidence and measured results
Primary ASU HTTPS news article with named PIs/co-PIs, NSF grant number, and technical aims. No production SLA, cost-per-job, or independent security evaluation of PARAPET yet—prototype phase.
Limitations and uncertainty
Award announcement, not measured deployment outcomes. Multi-year build; national-scale readiness is aspirational. Transfer to non-research admin data stores is not demonstrated.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-09-21; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Institutions cannot easily compute together on sensitive or regulated datasets without violating privacy/legal constraints.
- Stakeholders
- VPR / research computing
- CISO
- Research compliance
- CIO
- Domain researchers
- Discovery questions
- Which regulated datasets are blocked from multi-institution analysis today?
- Do data-sharing agreements already contemplate PETs?
- Who operates regulated research environments now?
- Is federated model training a near-term requirement?
- Value hypothesis
- A national PET testbed can reduce barriers to collaborative analysis on sensitive data if policy and infrastructure mature—outcomes not yet measured in this source.
- Potential engagement
- Readiness assessment of regulated datasets, agreements, and compute zones against PET pilot criteria.
- Unsupported claims
- Immediate production privacy guarantees
- Guaranteed quantum-safe protection today
- Direct administrative ERP productivity gains
Pre-sales engineering
Role takeaway
- Fit
- Strongest for R1/R2 campuses with regulated research portfolios; limited near-term fit for pure admin chatbot programs.
- Architecture and integration
- Federated training/update sharing, encrypted computation paths, and locked access to approved users/networks rather than central raw-data ingestion.
- Prerequisites
- Regulated network enclaves
- Data-sharing agreement processes
- PET-aware research computing staff
- Compliance review capacity
- Constraints
- Prototype timeline measured in years
- Hardware/software specialization costs
- Unproven transfer to campus admin systems
- Security
- Design for data inaccessible on compromise and PETs beyond naive anonymization; bind access to approved identities and networks.
- Proof of value
- Documented policy mapping for one regulated use case
- Successful encrypted or federated compute trial without raw-data export
- User onboarding checklist approved by compliance
Delivery
Role takeaway
- Work
- Prototype environment build, compliance alignment, national user recruitment/support, and PET operations runbooks.
- Dependencies
- NSF project partners
- Research Technology Office
- Sponsored research compliance
- Network security
- Ownership
- Research technology leadership with CISO and compliance co-ownership for production-like controls.
- Skills and adoption
- Train researchers and support staff on PET limitations, agreement requirements, and secure workflow steps.
- Governance checkpoints
- Policy fit review before each new data domain
- Access approval for secured networks
- Periodic re-assessment of PET threat assumptions
- Acceptance criteria
- Named compliance owner for PARAPET-like pilots
- Written data-sharing pathway using PETs
- Test report showing no raw regulated data left the enclave unexpectedly
- Risks
- Policy lag behind technical capability
- User support backlog
- Misunderstanding PETs as perfect anonymization
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
Plan for federated updates rather than central raw-data lakes; evaluate trusted execution/homomorphic paths and quantum-resistant considerations as part of regulated research computing roadmaps.
Governance
Who approves, reviews and stays accountable for outcomes?
Ellis’s compliance leadership and Regulated Research Community of Practice framing stress fitting PETs into institutional policies, workflows, and data-sharing agreements before national user onboarding.
Security and privacy
What data, permissions and controls need testing?
Aims to keep data inaccessible if devices are compromised and to avoid re-identification weaknesses of simple anonymization; success still depends on correct policy binding and approved-network access controls.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Builds specialist capacity in PETs and regulated research ops; recruitment of national test users implies training and support workload for research technology offices.
Procurement
What should contracts, pricing and exit terms secure?
NSF-funded cyberinfrastructure implies shared community resource economics rather than a single-campus SaaS buy; campuses evaluating PETs should expect hardware/software specialization and compliance review costs.
Operating model
Which teams own the service once it runs?
Positions research technology + cybersecurity + compliance as joint operators of privacy-preserving compute—not a pure science silo—especially where AI fine-tuning on sensitive data is desired.
Publication history
- 2026-09-21Campus Operations · Issue 154 resources
Stable resource ID: asu-sdsu-utah-parapet-privacy-ci-20260918