Evidence Receipt. Related Resources.
EPDQ: Efficient and Privacy-Preserving Exact Distance Query on Encrypted Graphs
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Canonical route: /signal-canvas/epdq-efficient-and-privacy-preserving-exact-distance-query-on-encrypted-graphs
- Proof freshness
- stale
- Proof status
- unverified
- Display score
- 7/10
- Last proof check
- 2026-03-30
- Score updated
- 2026-04-02
- Score fresh until
- 2026-05-02
- References
- 31
- Source count
- 3
- Coverage
- 50%
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Agent Handoff
EPDQ: Efficient and Privacy-Preserving Exact Distance Query on Encrypted Graphs
Canonical ID epdq-efficient-and-privacy-preserving-exact-distance-query-on-encrypted-graphs | Route /signal-canvas/epdq-efficient-and-privacy-preserving-exact-distance-query-on-encrypted-graphs
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/epdq-efficient-and-privacy-preserving-exact-distance-query-on-encrypted-graphsMCP example
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}Preparing verified analysis
Dimensions overall score 7.0
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Claim map
- Evidencepartial
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.
ImplicationpartialThis is explicitly stated in the abstract as a core component of the proposed method.
Verificationpartialpartial
- Evidencepartial
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.
ImplicationpartialThe abstract clearly states the introduction and benefits of the tensorized representation.
Verificationpartialpartial
- Evidencepartial
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.
ImplicationpartialThe abstract summarizes the experimental results, highlighting these advantages.
Verificationpartialpartial
- Evidencepartial
Moreover, it provides strong privacy protection guarantees, making it well suited for privacy-preserving graph query applications in cloud computing and distributed environments.
ImplicationpartialThe abstract explicitly mentions the privacy guarantees provided by the scheme.
Verificationpartialpartial
- Evidencepartial
Moreover, it provides strong privacy protection guarantees, making it well suited for privacy-preserving graph query applications in cloud computing and distributed environments.
ImplicationpartialThis is a direct conclusion drawn from the scheme's properties as stated in the abstract.
Verificationpartialpartial
- Evidencepartial
Definition 3.The EPDQ scheme is a set of six polynomial-time algorithms.
ImplicationpartialThis is a formal definition provided in the paper.
Verificationpartialpartial
- Evidencepartial
Theorem 2.The EPDQ scheme is indistinguishable under the leakage functionLin the CQA model for any PPT adversary.
ImplicationpartialThis is a formal security guarantee stated as a theorem.
Verificationpartialpartial
- Evidencepartial
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.
ImplicationpartialThe abstract explicitly states the integration of these two techniques as part of the proposed method.
Verificationpartialpartial
- Evidencepartial
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.
ImplicationpartialThe abstract clearly states the introduction and purpose of the tensorized representation.
Verificationpartialpartial
- Evidencepartial
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.
ImplicationpartialThe abstract mentions extensive experimental evaluations demonstrating superior scalability.
Verificationpartialpartial
- Evidencepartial
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.
ImplicationpartialThe abstract mentions extensive experimental evaluations demonstrating lower computational costs.
Verificationpartialpartial
- Evidencepartial
Moreover, it provides strong privacy protection guarantees, making it well suited for privacy-preserving graph query applications in cloud computing and distributed environments.
ImplicationpartialThe abstract explicitly states that the scheme provides strong privacy protection guarantees.
Verificationpartialpartial
