Paper Citation Record · LEDGER

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning

As of 16 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2412.01541.

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

pith.paper-citation-record.v1
2412.01541 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:21:45.675437Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

29 of 29 outbound references displayed

verified exact1
verified fuzzy17
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ef900075-8172-4153-b8cb-2d17c48d2503 · outbound

This paper cites In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security.

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.937381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 13af8688-0194-44cc-9a98-2eb8e1940d5a · outbound

This paper cites IEEE Internet of Things Journal 7(7), 5827–5842 (2019) Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning 17.

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning IEEE Internet of Things Journal 7(7), 5827–5842 (2019) Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning 17

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.928483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3a92febc-67b0-40bf-a98a-6041e1adfc03 · outbound

This paper cites Test15, 271–344 (2006).

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning Test15, 271–344 (2006)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.917203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f3e1fab7-1f56-47ce-9d87-d14600034fda · outbound

This paper cites Mathematics 10(8), 1283 (2022).

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning Mathematics 10(8), 1283 (2022)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.907506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 824f9942-417a-45ca-aaf8-5fdd8cd37e35 · outbound

This paper cites CRC Press (Oct 2018), http: //dx.doi.org/10.1201/9781315273570.

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning CRC Press (Oct 2018), http: //dx.doi.org/10.1201/9781315273570

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:45.600340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 715acd71-2587-4e1b-b707-53ee3fed42a1 · outbound

This paper cites ithaca-project.eu/, [Accessed 2024-11-27].

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning ithaca-project.eu/, [Accessed 2024-11-27]

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.897478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 66c4315c-6425-46da-ad15-8ea4dab5c8f4 · outbound

This paper cites In: 2021 Interna- tional Joint Conference on Neural Networks (IJCNN).

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning In: 2021 Interna- tional Joint Conference on Neural Networks (IJCNN)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.888084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6269fc1c-930a-458d-8c8f-30642e5dcc95 · outbound

This paper cites https://www.kaggle.com/ datasets/ashwiniyer176/toxic-tweets-dataset/data (2020), [Accessed 2024- 04-22].

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning https://www.kaggle.com/ datasets/ashwiniyer176/toxic-tweets-dataset/data (2020), [Accessed 2024- 04-22]

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.878330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2fff0097-78f9-460e-9cd9-b33a6857c5c7 · outbound

This paper cites In: Chiappa, S., Calandra, R.

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning In: Chiappa, S., Calandra, R

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.868265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 49cefa76-fedb-4b4f-9c00-f4a966d03cda · outbound

This paper cites On the Effectiveness of Regularization Against Membership Inference Attacks.

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning On the Effectiveness of Regularization Against Membership Inference Attacks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:45.617171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a252fb2-c136-4256-a415-5db55210832c · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning Adam: A Method for Stochastic Optimization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:45.620598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5db804a4-0a50-40b4-975f-c4c50d6a891a · outbound

This paper cites an unresolved cited work.

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:45.623636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af0ea7ba-f8d1-4eb9-9846-b73111b2aac2 · outbound

This paper cites Proceedings of the IEEE 86(11), 2278–2324 (1998).

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning Proceedings of the IEEE 86(11), 2278–2324 (1998)

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:45.626503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88f9fd66-6d82-4ffb-9b86-9e2ff5ee2c26 · outbound

This paper cites ACM Computing Surveys (CSUR) 54(2), 1–36 (2021).

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning ACM Computing Surveys (CSUR) 54(2), 1–36 (2021)

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:45.629683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f8a5fb5-16ef-4314-a699-7fdc9f9e549a · outbound

This paper cites In: International Conference on Machine Learning.

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning In: International Conference on Machine Learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.847893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e10b0fbd-8f21-4a88-a181-52b89e8ffaaa · outbound

This paper cites In: 2017 IEEE international conference on data mining (ICDM).

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning In: 2017 IEEE international conference on data mining (ICDM)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.836733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 99e1c6f8-443f-4d2c-a7c5-a34f6312bcbd · outbound

This paper cites Neural Networks 61, 85–117 (Jan 2015).

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning Neural Networks 61, 85–117 (Jan 2015)

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:45.638876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a844af3a-5750-4167-919c-7e535558e2b4 · outbound

This paper cites Cambridge University Press (2014).

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning Cambridge University Press (2014)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.827441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f50ea425-bd6d-473e-8846-b6c64170554e · outbound

This paper cites Inter- national Journal of Engineering Applied Sciences and Technology 04(12), 310–316 (May 2020).

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning Inter- national Journal of Engineering Applied Sciences and Technology 04(12), 310–316 (May 2020)

Reference 20

Resolution: verified exact; doi, observed 2026-08-12T04:21:45.700875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2ffe346b-0457-4199-99d9-19ca26278834 · outbound

This paper cites In: 2017 IEEE symposium on security and privacy (SP).

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning In: 2017 IEEE symposium on security and privacy (SP)

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:45.648152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8f898d9-5e12-4e75-b527-9c1c61929044 · outbound

This paper cites Harvard university press (2010) 18 N.

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning Harvard university press (2010) 18 N

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.811862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 87600f2e-0c59-4913-9ec2-38425ec8677a · outbound

This paper cites In: 30th USENIX Security Symposium (USENIX Security 21).

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning In: 30th USENIX Security Symposium (USENIX Security 21)

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.801640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bec5955a-271e-44aa-a2c6-e4aef19a29b4 · outbound

This paper cites IEEE Access8, 167425– 167447 (2020).

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning IEEE Access8, 167425– 167447 (2020)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.792499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 43dc39b8-a86f-4527-8c1c-ff9c6e4ed9e5 · outbound

This paper cites Analysis and Optimization of Convolutional Neural Network Architectures.

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning Analysis and Optimization of Convolutional Neural Network Architectures

Reference 25

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T04:21:45.734501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d2f876f4-ed74-42ad-b28f-6c0b70d0a889 · outbound

This paper cites Gaithersburg, MD (2024).

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning Gaithersburg, MD (2024)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.783243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6a6cf8de-b0b3-43d1-ada3-7636eabba7f9 · outbound

This paper cites Privacy-Preserving Machine Learning: Methods, Challenges and Directions.

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:45.667017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ee862ff-6d52-4dc1-a1dd-bae4448a789a · outbound

This paper cites In: 2018 IEEE 31st computer security foundations symposium (CSF).

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning In: 2018 IEEE 31st computer security foundations symposium (CSF)

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:45.669988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5859f1d-d430-4276-bf23-62e832bb7cdc · outbound

This paper cites In: Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice.

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning In: Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.769264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 38d6e2aa-9f4c-46ba-bde3-20eef2742d8e · outbound

This paper cites Procedia Computer Science 169, 393–399 (2020).

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning Procedia Computer Science 169, 393–399 (2020)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:45.760408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.