This canonical paper page includes Commercialization Proof and Related Resources.
ARXIV:2604.04696 · PRIVACY-PRESERVING COMPUTATION · SUBMITTED 07 APR · 20:11 UTC · FRESHNESS UNKNOWN
ARXIV:2604.04696PRIVACY-PRESERVING COMPUTATIONSUBMITTED 07 APR · 20:11 UTCFRESHNESS UNKNOWNHyesung Ji · Hyunah Yu · Jongmin Kim · Wonseok Choi · G. Edward Suh · Jung Ho Ahn · arXiv
GPIR accelerates private information retrieval on GPUs by re-architecting kernel design and data layout, achieving significant throughput gains over existing solutions.
Opportunity summary
Pain GPIR accelerates private information retrieval on GPUs by re-architecting kernel design and data layout, achieving significant throughput gains over existing solutions.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
GPIR accelerates private information retrieval on GPUs by re-architecting kernel design and data layout, achieving significant throughput gains over existing solutions. Modern lattice-based PIR protocols typically involve three phases: ExpandQuery (expanding a query into…
Private information retrieval (PIR) allows private database queries but is hindered by intense server-side computation and memory traffic. Modern lattice-based PIR protocols typically involve three phases: ExpandQuery (expanding a query into encrypted indices), RowSel…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. GPUs are theoretically ideal for these tasks, provided multi-client batching is used to achieve high throughput.
Privacy-Preserving Computation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Continue into Read for claims, analysis, references, and neighboring papers.
mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
GPIR accelerates private information retrieval on GPUs by re-architecting kernel design and data layout, achieving significant throughput gains over existing solutions.
Loading BUILD…
Paper Pack
10.48550/arXiv.2604.04696GPIR accelerates private information retrieval on GPUs by re-architecting kernel design and data layout, achieving significant throughput gains over existing solutions.
Abstract
Private information retrieval (PIR) allows private database queries but is hindered by intense server-side computation and memory traffic. Modern lattice-based PIR protocols typically involve three phases: ExpandQuery (expanding a query into encrypted indices), RowSel (encrypted row selection), and ColTor (recursive "column tournament" for final selection). ExpandQuery and ColTor primarily perform number-theoretic transforms (NTTs), whereas RowSel reduces to large-scale independent matrix-matrix multiplications (GEMMs). GPUs are theoretically ideal for these tasks, provided multi-client batching is used to achieve high throughput. However, batching fundamentally reshapes performance bottlenecks; while it amortizes database access costs, it expands working sets beyond the L2 cache capacity, causing divergent memory behaviors and excessive DRAM traffic. We present GPIR, a GPU-accelerated PIR system that rethinks kernel design, data layout, and execution scheduling. We introduce a stage-aware hybrid execution model that dynamically switches between operation-level kernels, which execute each primitive operation separately, and stage-level kernels, which fuse all operations within a protocol stage into a single kernel to maximize on-chip data reuse. For RowSel, we identify a performance gap caused by a structural mismatch between NTT-driven data layouts and tiled GEMM access patterns, which is exacerbated by multi-client batching. We resolve this through a transposed-layout GEMM design and fine-grained pipelining. Finally, we extend GPIR to multi-GPU systems, scaling both query throughput and database capacity with negligible communication overhead. GPIR achieves up to 305.7x higher throughput than PIRonGPU, the state-of-the-art GPU implementation.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 0% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
GPIR accelerates private information retrieval on GPUs by re-architecting kernel design and data layout, achieving significant throughput gains over existing solutions. Modern lattice-based PIR protocols typically involve three phases: ExpandQuery (expanding a query into encrypt...
METHOD
Private information retrieval (PIR) allows private database queries but is hindered by intense server-side computation and memory traffic. Modern lattice-based PIR protocols typically involve three phases: ExpandQuery (expanding a query into encrypted indices), RowSel (encrypted...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. GPUs are theoretically ideal for these tasks, provided multi-client batching is used to achieve high throughput.
WHY NOW
Privacy-Preserving Computation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
GPIR accelerates private information retrieval on GPUs by re-architecting kernel design and data layout, achieving significant throughput gains over existing solutions. Modern lattice-based PIR protocols typically involve three phases: ExpandQuery (expanding a query into encrypted indices), RowSel (encrypted row selection), and ColTor (recursive "column tournament" for final selection).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Private information retrieval (PIR) allows private database queries but is hindered by intense server-side computation and memory traffic. Modern lattice-based PIR protocols typically involve three phases: ExpandQuery (expanding a query into encrypted indices), RowSel (encrypted row selection), and ColTor (recursive "column tournament" for final selection).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. GPUs are theoretically ideal for these tasks, provided multi-client batching is used to achieve high throughput.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Privacy-Preserving Computation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.
Concepts
Methods
Materials
Markets
Competitors
GPIR accelerates private information retrieval on GPUs by re-architecting kernel design and data layout, achieving significant throughput gains over existing solutions.
Segment
Privacy-Preserving Computation
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
Direct
Adjacent
Substitute
Unknown
No indexed public discussion is attached to 2604.04696 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.
Hacker News
Not indexed yet
Not indexed yet
Bluesky
Not indexed yet
Preview the source document here, or use the hero PDF action for a new tab.
Reference metadata is not materialized in the public index yet. The source PDF remains the authority; cache refresh is optional.
CITED BY
No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.
Foundation
Extension
Commercially relevant
Conflicting
Owned Distribution
Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.
0/3 checks · 0%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 0 sources / 0% coverage
unknown
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
unknown
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
unknown
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 0% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
Next test
Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
No verified watchtower monitor rows yet.
FORESIGHT
No prediction yet — minted on next Foresight batch.
OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
No verified OpportunityKernel changes since the last view.
COMPETITIVE LANDSCAPE UPDATES
No verified competitive landscape changes yet.
RELATED PAPER UPDATES
No verified related paper changes yet.
SIGNAL CANVAS HISTORY AND DELTAS
No Signal Canvas history deltas yet.
TIMELINE
Save this paper to start tracking momentum - commits, demos, and score changes appear here.
No tracked events yet.
Score trend will appear after multiple data points.
BUZZ
Buzz trend pending.