{"resourceId":"asu-sdsu-utah-parapet-privacy-ci-20260918","versions":[{"version":"external-5917e24dff7b0817fd5b4f4cdc2f9c447809669cbd7621fdc3156ce45719b4da","resource":{"id":"asu-sdsu-utah-parapet-privacy-ci-20260918","title":"ASU–SDSU–Utah win $5M NSF award to build PARAPET privacy-enhancing cyberinfrastructure","organization":"Arizona State University with San Diego State University and University of Utah","sector":"Higher education research computing / privacy cyberinfrastructure","geography":"United States (multi-university NSF cyberinfrastructure)","publishedAt":"September 18, 2026 (ASU News)","publicationDate":"2026-09-18","eventDate":"2026-09-18","sourceName":"Arizona State University News","sourceLabel":"University news on NSF PARAPET privacy-enhancing technologies award","sourceUrl":"https://news.asu.edu/20260918-science-and-technology-5m-nsf-award-enhance-data-protections-users-nationwide","evidenceClass":"independent-reporting","outcomeClass":"emerging","topics":["infrastructure","data-security","governance-procurement","operating-model"],"finding":"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.","sledRelevance":"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":"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.","architectureImplications":"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.","governanceImplications":"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.","securityPrivacyImplications":"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.","caveats":"Award announcement, not measured deployment outcomes. Multi-year build; national-scale readiness is aspirational. Transfer to non-research admin data stores is not demonstrated.","streamIds":["campus-operations"],"roles":{"sales":"Buyers wrestling with collaborative analysis on regulated datasets need PETs, not another open data lake. PARAPET is a funded national prototype—not a productized campus appliance with SLAs. Bound conversations to regulated-research collaboration and AI training without raw-data transfer; do not claim production-ready privacy guarantees from this award notice.","engineering":"Map differential privacy, homomorphic encryption, federated learning, and secure hardware into existing research-computing and identity boundaries. Prerequisites include data-sharing agreement templates, regulated network zones, and compliance owners. Constraints: five-year prototype, no performance numbers here. Proof of value is a policy-aligned pilot use case—not a grant press quote.","delivery":"Delivery work centers on standing up compliant environments, user onboarding from the regulated-research community, and PET workflow documentation. Ownership spans research technology, CISO, and sponsored-research compliance. Acceptance criteria should include policy fit checks and controlled test datasets. Risks: overselling PETs as anonymization replacements and underestimating support staffing."},"roleDetails":{"sales":{"customerProblem":"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"],"discoveryQuestions":["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?"],"valueHypothesis":"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.","potentialEngagement":"Readiness assessment of regulated datasets, agreements, and compute zones against PET pilot criteria.","unsupportedClaims":["Immediate production privacy guarantees","Guaranteed quantum-safe protection today","Direct administrative ERP productivity gains"]},"engineering":{"fit":"Strongest for R1/R2 campuses with regulated research portfolios; limited near-term fit for pure admin chatbot programs.","architectureIntegration":"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.","proofOfValue":["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":{"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.","skillsAdoption":"Train researchers and support staff on PET limitations, agreement requirements, and secure workflow steps.","governanceCheckpoints":["Policy fit review before each new data domain","Access approval for secured networks","Periodic re-assessment of PET threat assumptions"],"acceptanceCriteria":["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"]}},"retrievedAt":"2026-09-21T22:06:12-05:00","enrichedAt":"2026-09-21T22:08:00-05:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Builds specialist capacity in PETs and regulated research ops; recruitment of national test users implies training and support workload for research technology offices.","procurementImplications":"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.","operatingModelImplications":"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.","sourceVerification":{"openedUrl":"https://news.asu.edu/20260918-science-and-technology-5m-nsf-award-enhance-data-protections-users-nationwide","referenceExcerpt":"Using privacy-enhancing technologies, PARAPET is designed to be a more secure way to share and collaborate on sensitive data","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}