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ARXIV:2604.00169 · PRIVACY-PRESERVING ML · SUBMITTED 02 APR · 20:56 UTC · FRESHNESS STALE
ARXIV:2604.00169PRIVACY-PRESERVING MLSUBMITTED 02 APR · 20:56 UTCFRESHNESS STALEPengzhi Huang · Kiwan Maeng · G. Edward Suh · arXiv
A system-level characterization of MPC and FHE for privacy-preserving machine learning, evaluating performance, energy, and cost across various scenarios.
Opportunity summary
Pain A system-level characterization of MPC and FHE for privacy-preserving machine learning, evaluating performance, energy, and cost across various scenarios.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A system-level characterization of MPC and FHE for privacy-preserving machine learning, evaluating performance, energy, and cost across various scenarios. Privacy-preserving machine learning (PPML) has attracted growing research attention, with approaches such as secure multiparty…
Privacy protection has become an increasing concern in modern machine learning applications. Privacy-preserving machine learning (PPML) has attracted growing research attention, with approaches such as secure multiparty computation (MPC) and fully homomorphic encryption (FHE)…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. This work provides system-level insights for researchers and practitioners who seek to understand or accelerate PPML workloads.
Privacy-Preserving ML moved forward this cycle; last verified April 2026. Public score 3.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score3.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A system-level characterization of MPC and FHE for privacy-preserving machine learning, evaluating performance, energy, and cost across various scenarios.
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Paper Pack
10.48550/arXiv.2604.00169A system-level characterization of MPC and FHE for privacy-preserving machine learning, evaluating performance, energy, and cost across various scenarios.
Abstract
Privacy protection has become an increasing concern in modern machine learning applications. Privacy-preserving machine learning (PPML) has attracted growing research attention, with approaches such as secure multiparty computation (MPC) and fully homomorphic encryption (FHE) being actively explored. However, existing evaluations of these approaches have frequently been done on a narrow, fragmented setup and only focused on a specific performance metric, such as the online inference latency of a specific batch size. From the existing reports, it is hard to compare different approaches, especially when considering other metrics like energy/cost or broader system setups (various hyperparameters, offline overheads, future hardware/network configurations, etc.). We present a unified characterization of three popular approaches -- two variants of MPC based on arithmetic/binary sharing conversion and function secret sharing, and FHE -- on their performance and cost in performing privacy-preserving inference on multiple CNN and Transformer models. We study a range of LAN and WAN environments, model sizes, batch sizes, and input sequence lengths. We evaluate not only the performance but also the energy consumption and monetary cost of deploying under a realistic scenario, taking into account their offline and online computation/communication overheads. We provide empirical guidance for selecting, optimizing, and deploying these privacy-preserving compute paradigms, and outline how evolving hardware and network trends are likely to shift trade-offs between the two MPC schemes and FHE. This work provides system-level insights for researchers and practitioners who seek to understand or accelerate PPML workloads.
Source availability
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Extraction status
Parse run pending anchorsA parse run id is attached, but no public source anchors are materialized yet.
Proof status
unverified0 refs; 0 sources; 17% 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 3.0
PROBLEM
A system-level characterization of MPC and FHE for privacy-preserving machine learning, evaluating performance, energy, and cost across various scenarios. Privacy-preserving machine learning (PPML) has attracted growing research attention, with approaches such as secure multipar...
METHOD
Privacy protection has become an increasing concern in modern machine learning applications. Privacy-preserving machine learning (PPML) has attracted growing research attention, with approaches such as secure multiparty computation (MPC) and fully homomorphic encryption (FHE) be...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. This work provides system-level insights for researchers and practitioners who seek to understand or accelerate PPML workloads.
WHY NOW
Privacy-Preserving ML moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A system-level characterization of MPC and FHE for privacy-preserving machine learning, evaluating performance, energy, and cost across various scenarios. Privacy-preserving machine learning (PPML) has attracted growing research attention, with approaches such as secure multiparty computation (MPC) and fully homomorphic encryption (FHE) being actively explored.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Privacy protection has become an increasing concern in modern machine learning applications. Privacy-preserving machine learning (PPML) has attracted growing research attention, with approaches such as secure multiparty computation (MPC) and fully homomorphic encryption (FHE) being actively explored.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. This work provides system-level insights for researchers and practitioners who seek to understand or accelerate PPML workloads.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Privacy-Preserving ML moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
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Competitors
A system-level characterization of MPC and FHE for privacy-preserving machine learning, evaluating performance, energy, and cost across various scenarios.
Segment
Privacy-Preserving ML
Adoption evidence
No public code link in the paper record yet
Commercial read
3.0/10 public viability
Direct
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Substitute
Unknown
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CITED BY
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Commercially relevant
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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.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 0 sources / 17% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
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, 17% evidence coverage.
Gaps
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
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Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
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Gaps
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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
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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
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
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No CRM or outreach source attached.
People
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Gaps
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Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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BUZZ
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