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ARXIV:2603.26219 · PRIVACY-PRESERVING GRAPH DATABASES · SUBMITTED 30 MAR · 21:54 UTC · FRESHNESS STALE
ARXIV:2603.26219PRIVACY-PRESERVING GRAPH DATABASESSUBMITTED 30 MAR · 21:54 UTCFRESHNESS STALEXuemei Fu · arXiv
Enables efficient and private shortest distance queries on encrypted graph databases using a novel tensor-based indexing scheme.
Opportunity summary
Pain Enables efficient and private shortest distance queries on encrypted graph databases using a novel tensor-based indexing scheme.
Evidence 31 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Enables efficient and private shortest distance queries on encrypted graph databases using a novel tensor-based indexing scheme. Among various graph operations, shortest distance queries play a fundamental role in numerous applications, such as path…
With the explosive growth of graph-structured data, graph databases have become a critical infrastructure for supporting large-scale and complex data analysis. Among various graph operations, shortest distance queries play a fundamental role in numerous…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. However, existing encrypted graph query methods still suffer from limitations in computational efficiency and system scalability, making it challenging to support efficient query processing…
Privacy-Preserving Graph Databases moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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Enables efficient and private shortest distance queries on encrypted graph databases using a novel tensor-based indexing scheme.
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Paper Pack
10.48550/arXiv.2603.26219Enables efficient and private shortest distance queries on encrypted graph databases using a novel tensor-based indexing scheme.
Abstract
With the explosive growth of graph-structured data, graph databases have become a critical infrastructure for supporting large-scale and complex data analysis. Among various graph operations, shortest distance queries play a fundamental role in numerous applications, such as path planning, recommendation systems, and knowledge graphs. However, existing encrypted graph query methods still suffer from limitations in computational efficiency and system scalability, making it challenging to support efficient query processing over large-scale encrypted graph data. To address these challenges, this paper proposes a tensor-based shortest distance query scheme for encrypted graph databases. The proposed method integrates an encrypted 2-hop cover indexing framework with the Pruned Landmark Labeling (PLL) technique, thereby constructing an efficient and privacy-preserving indexing mechanism. Furthermore, a tensorized representation is introduced to uniformly model graph structures, which effectively reduces computational complexity while ensuring data privacy, and significantly improves the scalability of the system. Extensive experimental evaluations on large-scale graph datasets demonstrate that the proposed approach achieves superior scalability and lower computational costs compared with existing encrypted graph query methods. Moreover, it provides strong privacy protection guarantees, making it well suited for privacy-preserving graph query applications in cloud computing and distributed environments.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Parse run pending anchorsA parse run id is attached, but no public source anchors are materialized yet.
Proof status
unverified31 refs; 3 sources; 50% 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
Enables efficient and private shortest distance queries on encrypted graph databases using a novel tensor-based indexing scheme. Among various graph operations, shortest distance queries play a fundamental role in numerous applications, such as path planning, recommendation syst...
METHOD
With the explosive growth of graph-structured data, graph databases have become a critical infrastructure for supporting large-scale and complex data analysis. Among various graph operations, shortest distance queries play a fundamental role in numerous applications, such as pat...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. However, existing encrypted graph query methods still suffer from limitations in computational efficiency and system scalability, making it challenging to support efficient query processing over large-sca...
WHY NOW
Privacy-Preserving Graph Databases moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
The proposed method integrates an encrypted 2-hop cover indexing framework with the Pruned Landmark Labeling (PLL) technique, thereby constructing an efficient and privacy-preserving indexing mechanism.
This is explicitly stated in the abstract as a core component of the proposed method.
partial
Furthermore, a tensorized representation is introduced to uniformly model graph structures, which effectively reduces computational complexity while ensuring data privacy, and significantly improves the scalability of the system.
The abstract clearly states the introduction and benefits of the tensorized representation.
partial
Extensive experimental evaluations on large-scale graph datasets demonstrate that the proposed approach achieves superior scalability and lower computational costs compared with existing encrypted graph query methods.
The abstract summarizes the experimental results, highlighting these advantages.
partial
Moreover, it provides strong privacy protection guarantees, making it well suited for privacy-preserving graph query applications in cloud computing and distributed environments.
The abstract explicitly mentions the privacy guarantees provided by the scheme.
partial
Moreover, it provides strong privacy protection guarantees, making it well suited for privacy-preserving graph query applications in cloud computing and distributed environments.
This is a direct conclusion drawn from the scheme's properties as stated in the abstract.
partial
Definition 3.The EPDQ scheme is a set of six polynomial-time algorithms.
This is a formal definition provided in the paper.
partial
Theorem 2.The EPDQ scheme is indistinguishable under the leakage functionLin the CQA model for any PPT adversary.
This is a formal security guarantee stated as a theorem.
partial
The proposed method integrates an encrypted 2-hop cover indexing framework with the Pruned Landmark Labeling (PLL) technique, thereby constructing an efficient and privacy-preserving indexing mechanism.
The abstract explicitly states the integration of these two techniques as part of the proposed method.
partial
Furthermore, a tensorized representation is introduced to uniformly model graph structures, which effectively reduces computational complexity while ensuring data privacy, and significantly improves the scalability of the system.
The abstract clearly states the introduction and purpose of the tensorized representation.
partial
Extensive experimental evaluations on large-scale graph datasets demonstrate that the proposed approach achieves superior scalability and lower computational costs compared with existing encrypted graph query methods.
The abstract mentions extensive experimental evaluations demonstrating superior scalability.
partial
Extensive experimental evaluations on large-scale graph datasets demonstrate that the proposed approach achieves superior scalability and lower computational costs compared with existing encrypted graph query methods.
The abstract mentions extensive experimental evaluations demonstrating lower computational costs.
partial
Moreover, it provides strong privacy protection guarantees, making it well suited for privacy-preserving graph query applications in cloud computing and distributed environments.
The abstract explicitly states that the scheme provides strong privacy protection guarantees.
partial
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Concepts
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Materials
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Enables efficient and private shortest distance queries on encrypted graph databases using a novel tensor-based indexing scheme.
Segment
Privacy-Preserving Graph Databases
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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CITED BY
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Foundation
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Commercially relevant
Conflicting
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3/3 checks · 100%
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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Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
31 refs / 3 sources / 50% 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
partial
Current read
Research evidence exists; buyer urgency still needs source proof.
Evidence
31 references, 3 sources, 50% 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
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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
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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COMPETITIVE LANDSCAPE UPDATES
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
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