Paper Citation Record · LEDGER

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization

As of 10 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2607.08963.

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

pith.paper-citation-record.v1
2607.08963 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T05:28:27.662569Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T00:50:58.991713Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-03T06:16:11.147974Z

Reference resolution

53 of 53 outbound references displayed

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External citation measurements

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Outbound references

Observation c9456cfd-80aa-4213-8560-6cc34b5887db · outbound

This paper cites Improved accuracy for private con- tinual cardinality estimation in fully dynamic streams via matrix factorization.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Improved accuracy for private con- tinual cardinality estimation in fully dynamic streams via matrix factorization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 2d7772d0-d0db-4f6a-8030-058a8abf9d11 · outbound

This paper cites A smooth binary mechanism for efficient private continual observation.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization A smooth binary mechanism for efficient private continual observation

Reference 2

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Observation ecd20ce8-8c8b-4b03-974d-70416ba77b00 · outbound

This paper cites Count on your elders: Laplace vs Gaussian noise.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Count on your elders: Laplace vs Gaussian noise

Reference 3

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Observation 626c4643-0809-414f-b9c6-277b9d2fd960 · outbound

This paper cites The Binary Tree Mechanism is Optimal for Approximate Differentially Private Continual Counting.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization The Binary Tree Mechanism is Optimal for Approximate Differentially Private Continual Counting

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 747f88c3-9f2f-40eb-a429-b6393f89311b · outbound

This paper cites Differentially private histograms under continual observation: Streaming selection into the unknown.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Differentially private histograms under continual observation: Streaming selection into the unknown

Reference 5

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Observation 72238bd4-27af-4169-91b1-748c200ff05d · outbound

This paper cites Private and continual release of statistics.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Private and continual release of statistics

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 48af781a-5e3f-4b79-b19b-5d20e7a0705e · outbound

This paper cites Multi-epoch matrix factorization mechanisms for private machine learning.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Multi-epoch matrix factorization mechanisms for private machine learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 68a3a3ec-7ba1-40e6-825f-e52078916743 · outbound

This paper cites Denisov, H.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Denisov, H

Reference 8

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Observation 2992990c-7ef0-434f-a01c-9ff9cfa8b3a5 · outbound

This paper cites A general- ized binary tree mechanism for differentially private approximation of all-pair distances.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization A general- ized binary tree mechanism for differentially private approximation of all-pair distances

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation de233a29-ad18-4ffa-ab47-319d866c445a · outbound

This paper cites Efficient and near-optimal noise generation for streaming differential privacy.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Efficient and near-optimal noise generation for streaming differential privacy

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation cb28c00a-423d-4c82-ab24-810aee072b6d · outbound

This paper cites Calibrating noise to sen- sitivity in private data analysis.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Calibrating noise to sen- sitivity in private data analysis

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 2aba238e-63cd-4149-a93a-2ff06546ba6d · outbound

This paper cites Differential privacy under continual observation.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Differential privacy under continual observation

Reference 12

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Observation 30f596bb-7f9b-423b-a91e-9d547a5ae3a6 · outbound

This paper cites The power of factorization mechanisms in local and central differential privacy.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization The power of factorization mechanisms in local and central differential privacy

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation d9dadf57-faee-42ed-857e-0ef28d00dc43 · outbound

This paper cites Differentially private continual releases of streaming frequency moment estimations.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Differentially private continual releases of streaming frequency moment estimations

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 059d5ca4-3354-427c-9101-e0e319483206 · outbound

This paper cites Differentially private algorithms for graphs under continual observation.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Differentially private algorithms for graphs under continual observation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 023a6c21-71cd-4743-9372-668ea48c0ab2 · outbound

This paper cites Constant matters: Fine- grained complexity of differentially private continual observation using completely bounded norms.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Constant matters: Fine- grained complexity of differentially private continual observation using completely bounded norms

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 38fc82d0-322c-4c52-b27d-ffc3b553236b · outbound

This paper cites an unresolved cited work.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Unresolved cited work

Reference 17

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Observation 1ec646ee-3f5a-49bb-a3d4-2e3c8647bd35 · outbound

This paper cites Continual mean estimation under user-level privacy.Journal on Selected Areas in Information Theory, 2024.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Continual mean estimation under user-level privacy.Journal on Selected Areas in Information Theory, 2024

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation bee18981-8e51-4aa8-9aeb-58398265e1fb · outbound

This paper cites Kalinin, and Jalaj Upadhyay.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Kalinin, and Jalaj Upadhyay

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 1e2163a9-076f-4da0-86a4-58064a1c33c7 · outbound

This paper cites Differentially private continual release of histograms and related queries.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Differentially private continual release of histograms and related queries

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 5909a854-3261-4eb3-9165-423440b8cf12 · outbound

This paper cites Improved differentially private continual observation using group algebra.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Improved differentially private continual observation using group algebra

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 02e089f3-b1bc-412b-acec-21fb68887dfe · outbound

This paper cites Almost tight error bounds on differentially private continual counting.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Almost tight error bounds on differentially private continual counting

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation f61fb672-79c8-48ca-ada1-b6e19abf040c · outbound

This paper cites Efficient use of differentially private binary trees.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Efficient use of differentially private binary trees

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 61306dae-1a53-426d-8424-267291944679 · outbound

This paper cites Differentially private quantiles with smaller error.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Differentially private quantiles with smaller error

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation c37e6015-16ff-45f8-9611-23312bae9d28 · outbound

This paper cites Count- ing distinct elements in the turnstile model with differential privacy under continual observa- tion.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Count- ing distinct elements in the turnstile model with differential privacy under continual observa- tion

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation b231c6c5-4259-4d76-a092-cdd6b667d9c1 · outbound

This paper cites Practical and private (deep) learning without sampling or shuffling.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Practical and private (deep) learning without sampling or shuffling

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 20b764a3-bc99-425b-abed-6619f7b1255d · outbound

This paper cites Kalinin and Joel Daniel Andersson.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Kalinin and Joel Daniel Andersson

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 6900af8f-f347-4c43-80bb-fc5f6188fd18 · outbound

This paper cites Kalinin and Christoph H.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Kalinin and Christoph H

Reference 28

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Observation 313618ed-ee2e-46e0-b0da-6f629faf38f2 · outbound

This paper cites DP-{\lambda}CGD: Efficient Noise Correlation for Differentially Private Model Training.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization DP-{\lambda}CGD: Efficient Noise Correlation for Differentially Private Model Training

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation c6719d57-f564-4100-a954-e1b250ce7f61 · outbound

This paper cites Kalinin, Ryan McKenna, Jalaj Upadhyay, and Christoph H Lampert.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Kalinin, Ryan McKenna, Jalaj Upadhyay, and Christoph H Lampert

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 751d2c29-e039-464a-b6e5-dbc17439dd9a · outbound

This paper cites Kalinin, Aki Rehn, Joel Daniel Andersson, Antti Honkela, and Christoph H Lampert.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Kalinin, Aki Rehn, Joel Daniel Andersson, Antti Honkela, and Christoph H Lampert

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 47e23def-413e-4c17-99e6-286d54225859 · outbound

This paper cites Beyond Square Roots: Explicit Memory-Efficient Factorization for Multi-Epoch Private Learning.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Beyond Square Roots: Explicit Memory-Efficient Factorization for Multi-Epoch Private Learning

Reference 32

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Observation d42b0f92-cecb-4caa-ba55-4951cae675dc · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Adam: A Method for Stochastic Optimization

Reference 33

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Observation eaa398ed-9376-4953-8e6c-e49873dd282d · outbound

This paper cites Kwapie´ n and A.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Kwapie´ n and A

Reference 34

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Observation e5715513-aa80-4499-871a-a46aed47d537 · outbound

This paper cites Making old things new: a uni- fied algorithm for differentially private clustering.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Making old things new: a uni- fied algorithm for differentially private clustering

Reference 35

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Observation 7461ff39-979e-4f16-8756-9a137483cf10 · outbound

This paper cites The ma- trix mechanism: Optimizing linear counting queries under Differential Privacy.International Conference on Very Large Data Bases (VLDB), 2015.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization The ma- trix mechanism: Optimizing linear counting queries under Differential Privacy.International Conference on Very Large Data Bases (VLDB), 2015

Reference 36

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Observation 065a8c68-8c92-434d-bcf1-34062154456d · outbound

This paper cites On the limited memory BFGS method for large scale opti- mization.Mathematical programming, 1989.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization On the limited memory BFGS method for large scale opti- mization.Mathematical programming, 1989

Reference 37

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Observation f85543c4-4693-4640-96ff-a4d02040aaec · outbound

This paper cites Factorization norms and hereditary discrepancy.International Mathematics Research Notices, 2020.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Factorization norms and hereditary discrepancy.International Mathematics Research Notices, 2020

Reference 38

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Observation 25240bb5-4d8d-49c7-a48f-e241bf368c89 · outbound

This paper cites Smith, Mateusz Paprocki, Ondˇ rej ˇCert´ ık, Sergey B.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Smith, Mateusz Paprocki, Ondˇ rej ˇCert´ ık, Sergey B

Reference 39

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Observation 6acfbde3-2641-4dc5-8722-0a571b6f71a4 · outbound

This paper cites Correlated Noise Mechanisms for Differentially Private Learning.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Correlated Noise Mechanisms for Differentially Private Learning

Reference 40

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Observation 29ba1904-1dba-48f4-9cab-35858ac7527d · outbound

This paper cites Understanding hierarchical methods for differentially private histograms.Proc.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Understanding hierarchical methods for differentially private histograms.Proc

Reference 41

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 52a98f1e-f3cf-463b-807b-1490a07f9c35 · outbound

This paper cites Fully dynamic graph algorithms with edge differential privacy.Proceedings of the ACM on Management of Data (PACMMOD), 2024.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Fully dynamic graph algorithms with edge differential privacy.Proceedings of the ACM on Management of Data (PACMMOD), 2024

Reference 42

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Observation 5f8715f6-530e-4901-88e1-ca0c891c11d1 · outbound

This paper cites Sublinear space private algorithms under the sliding window model.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Sublinear space private algorithms under the sliding window model

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation ca7c1086-9a88-45e4-bcaa-ebeae19679b5 · outbound

This paper cites round” in the sensen= (2k+1)p−1 2 for somep, then the theorem holds. The remainder of the proof is a careful analysis of what happens fornthat falls in between “round.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization round” in the sensen= (2k+1)p−1 2 for somep, then the theorem holds. The remainder of the proof is a careful analysis of what happens fornthat falls in between “round

Reference 44

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation f5f3a9e8-9d43-4442-9189-1e374aef6177 · outbound

This paper cites Then|x j|=∥x∥ ∞.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Then|x j|=∥x∥ ∞

Reference 45

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 9f71fdf5-6351-4425-92a8-a4399ae51d79 · outbound

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Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Unresolved cited work

Reference 46

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Observation 18f12bb2-8011-4c34-9bbf-2f8165b2e5c4 · outbound

This paper cites an unresolved cited work.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Unresolved cited work

Reference 47

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation cece2c20-3e8f-4440-a975-104079f49f08 · outbound

This paper cites Then, sincea≤1 and−e≤1,D=af+b(−e)≤f+b.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Then, sincea≤1 and−e≤1,D=af+b(−e)≤f+b

Reference 48

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 59c383cc-2866-4b15-ad21-ca4a5f8d4845 · outbound

This paper cites LetJ= 0 1 −1 0 , so that det(x, y) =xJ y.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization LetJ= 0 1 −1 0 , so that det(x, y) =xJ y

Reference 49

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Observation eecd48d6-fa60-4bb6-8bb4-aa544ea4fa9e · outbound

This paper cites Otherwise,∥y∥ 2 2 = 2/3, and then∥x−y∥ 2 2 = 3 2 det(x, y)2, so Ψ(x, y) = 2/3>0.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Otherwise,∥y∥ 2 2 = 2/3, and then∥x−y∥ 2 2 = 3 2 det(x, y)2, so Ψ(x, y) = 2/3>0

Reference 50

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Observation 40d252d6-5c6e-4a48-92b3-465ade7b0438 · outbound

This paper cites Fixx, and suppose thatyis an interior minimizer.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Fixx, and suppose thatyis an interior minimizer

Reference 51

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Observation 8851448c-6769-491a-befd-0e8ca08ccc53 · outbound

This paper cites Otherwise,∥x∥ 2 2 = 4/3, andy= 1 2 x+ 3 4 det(x, y)J ′x.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Otherwise,∥x∥ 2 2 = 4/3, andy= 1 2 x+ 3 4 det(x, y)J ′x

Reference 52

Resolution: unresolved; no resolver link, observed 2026-07-13T05:28:27.662569Z

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Observation 1fd73ca9-840c-4d8f-8e7d-4f9e3f2cdedd · outbound

This paper cites Since Ψ is invariant under the symmetries of the square, we may assume thaty= (t,1), where−1≤t≤1.

Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization Since Ψ is invariant under the symmetries of the square, we may assume thaty= (t,1), where−1≤t≤1

Reference 53

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Pith citing papers

Observation 4cf75a3c-a87a-414c-b41e-23a22a1c7c62 · inbound

Costs of Arbitrary Real Matrix Factorizations for Pure-DP Continual Counting cites this paper.

Costs of Arbitrary Real Matrix Factorizations for Pure-DP Continual Counting Improved Error Bounds for Pure Differentially Private Continual Counting via Matrix Factorization

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-03T00:54:13.989646Z

Source-reported events for the cited work

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