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Paper Citation Record · LEDGER

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix

As of 10 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2507.06508.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.06508 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:10:19.436912Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

60 of 60 outbound references displayed

  • verified exact0
  • verified fuzzy49
  • unresolved9
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 98bec6d5-610a-4f6a-8e1a-65e2c26724f6 · outbound

This paper cites Hadamard response: Estimating distributions privately, efficiently, and with little communication.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Hadamard response: Estimating distributions privately, efficiently, and with little communication

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 64f3b419-031a-46e5-aa22-2c96592d8f2e · outbound

This paper cites S., G OULEAKIS , T., PEEBLES , J., R UBINFELD , R., AND YODPINYANEE , A.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix S., G OULEAKIS , T., PEEBLES , J., R UBINFELD , R., AND YODPINYANEE , A

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 774dea46-c12e-4958-8949-ed6fcb779350 · outbound

This paper cites Counting triangles in large graphs using ran- domized matrix trace estimation.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Counting triangles in large graphs using ran- domized matrix trace estimation

Reference 3

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 9f0a5173-de71-42f4-a74b-613aaf6f26eb · outbound

This paper cites The privacy blanket of the shuffle model.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix The privacy blanket of the shuffle model

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 89b4887c-3f4c-434a-a26c-6e03407a65af · outbound

This paper cites K., AND SESHADHRI , C.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix K., AND SESHADHRI , C

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.208912Z digest=sha256:4ef530f65df498de54f4b1e02c9d38320eef71ace5266bc3c7f97e7a2f258eba

Observation 9aa83368-b493-40b8-8879-9371d186dc30 · outbound

This paper cites W., SCHNEIDER , S., AND KERSCHBAUM , F.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix W., SCHNEIDER , S., AND KERSCHBAUM , F

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.213720Z digest=sha256:f6a3a8995da92bde2243c96fa3fbe29f6b20c404d0ef416b3b07654eeca0b1f8

Observation d12cfdae-70dc-4ab3-8ce8-ec780953d456 · outbound

This paper cites A privacy- preserving mechanism based on local differential pri- vacy in edge computing.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix A privacy- preserving mechanism based on local differential pri- vacy in edge computing

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.218370Z digest=sha256:fa08dfc69d2dc24eaf31e0eb597cd01b62faf6627663ea25b151c758fc549d2c

Observation 5ac99d6a-7196-4cc8-b979-ef3fa67802e4 · outbound

This paper cites Approximate counting of k-paths: Deterministic and in polynomial space.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Approximate counting of k-paths: Deterministic and in polynomial space

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.222408Z digest=sha256:dfdc41a4f29d2451653bd484d1e4cfeb9ef77664cd15114fef176eb8bbcf4547

Observation ef3cbb5f-305f-432d-b6c4-0e60be63ae80 · outbound

This paper cites Distributed differential privacy via shuf- fling.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Distributed differential privacy via shuf- fling

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.226123Z digest=sha256:f34b08baca5ef938e97e9a18f9391b11178ffebb1ac2adaac6865f189768b021

Observation 2c0ea86d-42c0-4c10-ade5-288cf3ba170e · outbound

This paper cites Arboricity and sub- graph listing algorithms.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Arboricity and sub- graph listing algorithms

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 9ac97b1d-fd91-4c3a-abac-f97635ac77a5 · outbound

This paper cites Matrix mul- tiplication via arithmetic progressions.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Matrix mul- tiplication via arithmetic progressions

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.234473Z digest=sha256:406c13c80270e487a0f6f298f5ef26a3591e54b81a0b66552e04edf2a3b419d8

Observation 5a45ae88-30dc-45c6-b65f-e9a7bb7e82ed · outbound

This paper cites an unresolved cited work.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Unresolved cited work

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.238632Z digest=sha256:d07ebe935c9647aed339ec18b52a7c36a425233a184fdde2f0a4ea31e0696cc4

Observation fc6a1c2e-913a-4bd1-a6bd-63fb236d78a9 · outbound

This paper cites Faster matrix mul- tiplication via asymmetric hashing.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Faster matrix mul- tiplication via asymmetric hashing

Reference 13

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.242677Z digest=sha256:0f50b5ad27cfa95f503eac16afdc06e557ed2257668a182e0ca274491734f25e

Observation b5a6bec9-0ccd-426b-97ad-50a1a44a3bbc · outbound

This paper cites Differential privacy.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Differential privacy

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.246744Z digest=sha256:e44fcad072ea7769887834a1dbaff060a5a7d36c74748532ca8afedb1997cc6f

Observation 5195ab32-8932-48c2-92ab-301a77e30b3a · outbound

This paper cites Calibrating noise to sensitivity in private data anal- ysis.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Calibrating noise to sensitivity in private data anal- ysis

Reference 15

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raw_fallback, observed 2026-08-06T19:10:20.130666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.250744Z digest=sha256:69c4f85ee3af4fc33e8d5b0f8aca13bff33575d4b0ca6fc7efae7002a06bf2f0

Observation 43c15dd8-d91b-4215-8784-6590356b1d58 · outbound

This paper cites The algorithmic founda- tions of differential privacy.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix The algorithmic founda- tions of differential privacy

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.254786Z digest=sha256:4c0831671be845fdf2c89bcef7ecdb85ef9ea74013d7ccc8874bbf833cf985be

Observation f2436c97-5958-4b3a-bfea-9a9b9d475778 · outbound

This paper cites Approximately counting triangles in sublinear time.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Approximately counting triangles in sublinear time

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.258657Z digest=sha256:9c3c95886f85aecaa0f3c7a1c240df956bf8e7c2205df17d692e652c02efc27c

Observation 40d4a52c-9c22-4309-9967-a299448a925c · outbound

This paper cites Triangle Counting with Local Edge Differential Privacy.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Triangle Counting with Local Edge Differential Privacy

Reference 18

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local_arxiv, observed 2026-08-06T19:10:19.499020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.262676Z digest=sha256:f2c9ed970ae543801d56a5696bedc2a50a9afc82039dfbaa1358208c933d4a45

Observation 8f48af87-0abb-448e-be46-a2d04dc9ef41 · outbound

This paper cites Amplification by shuffling: From local to central differential privacy via anonymity.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Amplification by shuffling: From local to central differential privacy via anonymity

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation f503929c-9bef-4f7e-b747-6d78ad1701a3 · outbound

This paper cites Hiding among the clones: A simple and nearly opti- mal analysis of privacy amplification by shuffling.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Hiding among the clones: A simple and nearly opti- mal analysis of privacy amplification by shuffling

Reference 20

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 694d0db7-53a5-48e0-a61f-32de447359c9 · outbound

This paper cites Counting stars and other small subgraphs in sublinear-time.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Counting stars and other small subgraphs in sublinear-time

Reference 21

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.275225Z digest=sha256:df870a929d15ba33ff35cd307fdc4f35b9711ef9fbdeb147257c3ae9d0daf61e

Observation 99b33cdb-e87f-430f-883c-01873b9cd1a2 · outbound

This paper cites Lo- cally differentially private analysis of graph statistics.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Lo- cally differentially private analysis of graph statistics

Reference 22

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.279272Z digest=sha256:9df99e36076845f5cf584d3421f68c7eb2a4d8b19066434b03728cc71e46c6c2

Observation 4a5f2439-5a13-4031-9190-8d40c9101a2a · outbound

This paper cites 983–1000.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix 983–1000

Reference 23

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.283942Z digest=sha256:02a1e06c9d0c3a641a2179b4e4caa1174107205ca0fb826b292f54b793c799d7

Observation ff886fb2-f81b-4f72-b29f-1338bd6227a1 · outbound

This paper cites {Communication-Efficient} triangle counting under lo- cal differential privacy.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix {Communication-Efficient} triangle counting under lo- cal differential privacy

Reference 24

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.287858Z digest=sha256:49b59cea92d28430df6bbd5b581b3a708b2a0ce3b31812970c82a739666fb97d

Observation 23544088-ed96-43fc-8652-6cf50f6b092c · outbound

This paper cites Dif- ferentially private triangle and 4-cycle counting in the shuffle model.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Dif- ferentially private triangle and 4-cycle counting in the shuffle model

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.291716Z digest=sha256:51b477c2df00860c829d16d28aad054a25e97d4dd3771a73b7f466900f3a49b9

Observation 32081c39-8b29-459b-8217-7bfe3a66e6e2 · outbound

This paper cites Publishing graphs under node differential privacy.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Publishing graphs under node differential privacy

Reference 26

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raw_fallback, observed 2026-08-06T19:10:19.984583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.296127Z digest=sha256:78e3123cd3c784ba21661d21800b1f363ac572ca9d6f9ef09b5f025c6e47efd5

Observation 6be7b2ab-bddc-4b9b-b5df-f2a6c90649d0 · outbound

This paper cites Dis- crete distribution estimation under local privacy.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Dis- crete distribution estimation under local privacy

Reference 27

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raw_fallback, observed 2026-08-06T19:10:19.969612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.300394Z digest=sha256:e2bb915ee7e9a7dd038b0cdbf552cc7ef87d5cc5a90fed5dc3951d4e82a5033b

Observation ccb3dc0b-ec93-4cae-b997-792f2df31be7 · outbound

This paper cites The complexity of counting cycles in the adjacency list streaming model.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix The complexity of counting cycles in the adjacency list streaming model

Reference 28

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raw_fallback, observed 2026-08-06T19:10:19.953606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.304680Z digest=sha256:4efeb1cf5f153dd05e5ac2f3eab3bbff06667de8756e0517deefbce2e88eaf3e

Observation f56d67e3-c056-4ec1-b3d1-2bf2919bc6c3 · outbound

This paper cites P., AND KIM, S.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix P., AND KIM, S

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.308825Z digest=sha256:2198da048f6312215dc421711aaef7e1b8c644602354d5249773fa85dbf8f1f1

Observation 203eb358-9b48-4c42-8acc-f7a829a9a922 · outbound

This paper cites P., N ISSIM , K., R ASKHOD - NIKOVA , S., AND SMITH , A.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix P., N ISSIM , K., R ASKHOD - NIKOVA , S., AND SMITH , A

Reference 30

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raw_fallback, observed 2026-08-06T19:10:19.925989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.312578Z digest=sha256:53575fde9bf3f9c2205598fbfd1e2544a387a7bc0c9b2fff3deac2b947e7d43e

Observation 51c10957-205a-4d42-9874-71aa93a0793a · outbound

This paper cites N., M ILLER , G.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix N., M ILLER , G

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.316971Z digest=sha256:cb40fe9a02e8ecb5cae22fddb4bafe638a92d2f25389fd7b9146761aa9523209

Observation c081d573-4561-486a-9adc-6af8980784a6 · outbound

This paper cites Powers of tensors and fast matrix multi- plication.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Powers of tensors and fast matrix multi- plication

Reference 32

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raw_fallback, observed 2026-08-06T19:10:19.898125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.320996Z digest=sha256:5a97e79bcd23a7496a3033c00fbea4b97a52d1c8f8b1558d7325fbd2462fa55f

Observation 4c947629-9f3f-45b5-816c-47ee24407003 · outbound

This paper cites Graph evolution: Densification and shrinking diameters.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Graph evolution: Densification and shrinking diameters

Reference 33

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raw_fallback, observed 2026-08-06T19:10:19.883109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.325367Z digest=sha256:530857a24247f4fbb4c1c903f95102d2596e947c75029817864c3664a3746708

Observation 7b5584b4-38e4-446f-a7c6-ab2c6867d678 · outbound

This paper cites SNAP Datasets: Stan- ford large network dataset collection.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix SNAP Datasets: Stan- ford large network dataset collection

Reference 34

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raw_fallback, observed 2026-08-06T19:10:19.869359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.329356Z digest=sha256:9c08df35f509023dc40cb074c5ff7f8564a27594849460e026c956bc3ac0d0ba

Observation bbec5c9f-d738-41b7-baa6-f7dcf48c2e34 · outbound

This paper cites Learning to dis- cover social circles in ego networks.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Learning to dis- cover social circles in ego networks

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.855831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.333448Z digest=sha256:b9a7ec2f22d9ee4e2a4d0b4cf18635e5fa463842c9fa3290a2c441fce2b4fcf9

Observation f333449b-6d89-4d6c-a644-90990efe0c87 · outbound

This paper cites Collecting triangle counts with edge relationship local differential privacy.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Collecting triangle counts with edge relationship local differential privacy

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.842786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.337786Z digest=sha256:7239ea625349e8e7881951bec94b5d50fb36ca9dbb67523680a07d1acb53d2f2

Observation b1a0ba05-c842-4d13-8af5-c9cde42ad36f · outbound

This paper cites Approximate counting of cycles in streams.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Approximate counting of cycles in streams

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.829456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.341638Z digest=sha256:542d0d07d911d0276baada4a942c2b0775126ac654c32af8b9469ae9df8c3634

Observation b9fafa62-d4a4-4347-bcc4-db9951067767 · outbound

This paper cites Triangle and four cycle counting in the data stream model.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Triangle and four cycle counting in the data stream model

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.814418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.345455Z digest=sha256:8bad9138d4f865d9c884da0706bc6f7f18cfc568bfbaf6af47aa44d743bb61ad

Observation 3d51a4a6-9aab-49ef-9c10-ad0b50c7dbdf · outbound

This paper cites an unresolved cited work.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Unresolved cited work

Reference 39

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:10:19.799970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.349371Z digest=sha256:c2d27c43ce619dc7db05c3abd30c3ea33f27ffc1b73cceb8c367fc889b2d7068

Observation 4669150d-7bb3-4a90-a26c-9b1fdda29ef0 · outbound

This paper cites The number of data breaches in 2021 has already surpassed last year’s total, 2021.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix The number of data breaches in 2021 has already surpassed last year’s total, 2021

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.785819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.353375Z digest=sha256:a5487a67149e288a1c46c924de63c3604984297b706afb7a0259c748a0f400b2

Observation 3fbbd61c-2f8c-4836-ab26-3cb612521674 · outbound

This paper cites {Utility- optimized} local differential privacy mechanisms for distribution estimation.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix {Utility- optimized} local differential privacy mechanisms for distribution estimation

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.771703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.357304Z digest=sha256:6a14d9106e8052e9f87af69a02fcff434968a5f2f367b0de5fbe4b8e1a4cf64d

Observation c0b7364a-df6e-4da4-ba27-f9e313e64a7d · outbound

This paper cites Local and Central Differential Privacy for Robustness and Privacy in Federated Learning.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Local and Central Differential Privacy for Robustness and Privacy in Federated Learning

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-06T19:10:19.361450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:10:19.361450Z digest=sha256:5f3f7b573a29982103b64e96e02994555133f9a4fa8c96c9c4c8051aabb75cc6

Observation 328bcf86-0609-44b9-9cd7-0bd90a607604 · outbound

This paper cites Faster approximate subgraph counts with privacy.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Faster approximate subgraph counts with privacy

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.757193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.365727Z digest=sha256:31fc167f8508d6998a915042cd9acae4c0089659d7fb387a988a9b3919e0f6bc

Observation 57f4f3c8-f326-4e56-ad63-96aadd61fb21 · outbound

This paper cites Smooth sensitivity and sampling in private data anal- ysis.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Smooth sensitivity and sampling in private data anal- ysis

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.743335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.370108Z digest=sha256:6bc03cae69e1353dc5b8bcbaf374a4f6fb73de3d2e150dc37b536019511bf9cf

Observation a08866ad-84a4-4d1d-a632-4e390e21be04 · outbound

This paper cites Triangle listing al- gorithms: Back from the diversion.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Triangle listing al- gorithms: Back from the diversion

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.729057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.374546Z digest=sha256:f3361d401d80ade705450a123a00cfdbdc21d1293e2a5caeedb636be461c9819

Observation 6c4bb8f1-c1fd-40db-bb31-3bc8ca951185 · outbound

This paper cites Generating synthetic decentralized social graphs with local differential privacy.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Generating synthetic decentralized social graphs with local differential privacy

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.715666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.378492Z digest=sha256:7bc3bb890c5aac5e01adfd3a73dabd9fc5630d8b1570bd4110d5de5a638892b3

Observation f40d30c0-c343-4556-b5ba-0beade384baf · outbound

This paper cites Differentially private analysis of graphs.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Differentially private analysis of graphs

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.702229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.382774Z digest=sha256:588a616b041579a75b0880376ddc4f6c7ea96f0271f0ed71b03227530b8d6af6

Observation c09d4228-c67a-4154-ac3d-b881b0b749e1 · outbound

This paper cites E., A PARICIO , D., AND SILVA, F.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix E., A PARICIO , D., AND SILVA, F

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.688384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.386687Z digest=sha256:4114de515f504a66a8237a113bf6302a70c859d00d5d1c415142f1bcced072c6

Observation eaed251d-cc55-4bf0-85fe-f05ee9bf0fda · outbound

This paper cites an unresolved cited work.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:10:19.675401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.390586Z digest=sha256:e59e5228ee2a3258e3d34ffc1edc16511a634ab6c9205a2c925a009795bcc3c0

Observation 90d6962a-ecbc-4e0f-a275-d15310d3f239 · outbound

This paper cites an unresolved cited work.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Unresolved cited work

Reference 50

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:10:19.662401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.394553Z digest=sha256:245f91f3ee612b60f0c9c4134e0ec49ac353a4427536d6d1d37ca21e2b010ee4

Observation 93560de0-9265-44fa-933c-0b8890d13ef8 · outbound

This paper cites an unresolved cited work.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Unresolved cited work

Reference 51

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:10:19.648515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.398419Z digest=sha256:de70227dbf5c950e9bd7a8866daba9479ce1670e2e4c725744e91ac7783ba4ae

Observation 998eb9f8-a44f-4ce2-ba34-60d695b98cdc · outbound

This paper cites Gaussian elimination is not optimal.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Gaussian elimination is not optimal

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.634649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.402868Z digest=sha256:6283d9e74f99ec0d7265f51a09caa2b91add35e2e769aa49e82e8ea9d60db5d4

Observation b813027a-08fc-4f1a-9055-4cee7e04a4cd · outbound

This paper cites E., K ANG , U., M ILLER , G.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix E., K ANG , U., M ILLER , G

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.620585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.407049Z digest=sha256:bb1d16849d58aaf8687604e6deb69bdf2a58d48aff47f7586fbd249f4732ea60

Observation 0e3dc45c-bbc8-4be6-a6d6-4389f92a7b2d · outbound

This paper cites an unresolved cited work.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Unresolved cited work

Reference 54

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:10:19.606270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.411091Z digest=sha256:70d41ed5aef9f006033d1171c1ce6c383c186039b74afb83978b62f73c0223ac

Observation 8a3326c0-ccab-420a-b5c9-33672648f4d3 · outbound

This paper cites Locally differentially private protocols for frequency estimation.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Locally differentially private protocols for frequency estimation

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.591873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.414925Z digest=sha256:2e0badb946515b8c0386293201ea47a1a08bd25dd924dd33f187524eeb8edd1f

Observation 15337bc7-64bb-437a-b393-c6e51ce897eb · outbound

This paper cites Edge-based differential privacy comput- ing for sensor–cloud systems.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Edge-based differential privacy comput- ing for sensor–cloud systems

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.576605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.419555Z digest=sha256:e0191c174d38ad742260552e09f3a1c7ad5edac9adaec1bcca6a528e5740c7d0

Observation 94be922f-2957-47a0-800f-4378c96cd8c0 · outbound

This paper cites Using randomized response for differential privacy preserving data col- lection.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Using randomized response for differential privacy preserving data col- lection

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.561086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.423657Z digest=sha256:08120f6c038ff04a42321c381ae36fbecd9b6140c29b76d503cce2caf98e329d

Observation e5bb9d94-9ae0-47cd-b792-fbb2cbb02e07 · outbound

This paper cites an unresolved cited work.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Unresolved cited work

Reference 58

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:10:19.544694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.428218Z digest=sha256:ea9e8e1d51a68ab9873e671ae6c67a94f65de56aa19e953414aff77c047a86f8

Observation 036f43fd-06ee-45a5-9ae9-9d6c6210d063 · outbound

This paper cites an unresolved cited work.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix Unresolved cited work

Reference 59

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:10:19.529390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.431979Z digest=sha256:85d50af2e1f7b3d1f3e597a46cbcebcef450f75e8604cb2529db758d0a2f7e19

Observation b1de0fd1-621c-43a3-b99d-03a22ee73e83 · outbound

This paper cites n1 ∏ i=1 Zki αi #2 + E.

Subgraph Counting under Edge Local Differential Privacy Based on Noisy Adjacency Matrix n1 ∏ i=1 Zki αi #2 + E

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:10:19.514574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T19:10:19.436912Z digest=sha256:a3544bc488d12cd50729ca392dd4ed86aeb8b606bb3c8a01fbfec32d55404740

Pith citing papers

No inbound Pith citation observations are available.