Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T14:36:35.797559Z
Paper Citation Record · LEDGER
As of 16 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:1908.02997.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T14:36:35.797559Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
51 of 51 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation e2ee45af-36be-46a8-812e-ff0362d10639 · outbound
Local Differential Privacy for Deep Learning Deep learning with differential privacy,
Reference 1
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b0744473-4661-4041-b006-d961dcfa6ef6 · outbound
Local Differential Privacy for Deep Learning Membership inference attacks against machine learning models,
Reference 2
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ad15fb4e-fdb7-4d2e-824d-93b6d1b19e83 · outbound
Local Differential Privacy for Deep Learning Tensorflow: a system for large- scale machine learning
Reference 3
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 662584df-7bde-4d6e-8042-25f0f93ef6b9 · outbound
Local Differential Privacy for Deep Learning Distributed graphlab: a framework for machine learning and data mining in the cloud,
Reference 4
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8472b1fe-daff-4b03-8f95-dae7eefd3b1b · outbound
Local Differential Privacy for Deep Learning Machine learning models that remember too much,
Reference 5
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 497b5006-aeb5-46d2-a75a-392d7dffcadc · outbound
Local Differential Privacy for Deep Learning Model inversion attacks that exploit confidence information and basic countermeasures,
Reference 6
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 70613b5e-630f-4efe-930d-ef6eb88a1a6b · outbound
Local Differential Privacy for Deep Learning The algorithmic foundations of differential privacy,
Reference 7
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 61f23fc8-266f-4b0b-99c6-fcb28345dfc2 · outbound
Local Differential Privacy for Deep Learning Privacy-preserving deep learning,
Reference 8
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a21e6670-d272-4e28-88b1-7352106fdd41 · outbound
Local Differential Privacy for Deep Learning Output perturbation with query relaxation,
Reference 9
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 30c4759e-b62c-40cc-8911-7795e13153a9 · outbound
Local Differential Privacy for Deep Learning Extremal mechanisms for local differential privacy,
Reference 10
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 22d83b17-ba35-4afa-bec8-82c3466b84d8 · outbound
Local Differential Privacy for Deep Learning Unresolved cited work
Reference 11
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2e3e0e20-5489-4948-b719-8a7d4dbea548 · outbound
Local Differential Privacy for Deep Learning An efficient and scalable privacy preserving algorithm for big data and data streams,
Reference 12
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation f96aada3-a78b-4a51-b1d8-6209c3be15c9 · outbound
Local Differential Privacy for Deep Learning Differentially private continual monitoring of heavy hitters from distributed streams,
Reference 13
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 12b510c9-a6fe-4f31-9641-d5dc71c4a279 · outbound
Local Differential Privacy for Deep Learning Rappor: Randomized aggre- gatable privacy-preserving ordinal response,
Reference 14
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation e363f7ad-7d3f-470d-bc7e-51ed06b26e7d · outbound
Local Differential Privacy for Deep Learning Randomized response: A survey technique for eliminating evasive answer bias,
Reference 15
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b74c0f5d-3bb0-491e-bd5c-32496990db5e · outbound
Local Differential Privacy for Deep Learning Using randomized response for differential privacy preserving data collection
Reference 16
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation e931c4d9-36ec-4e2b-a851-ab309ba77667 · outbound
Local Differential Privacy for Deep Learning Heavy hitter estimation over set-valued data with local differential privacy,
Reference 17
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 0f83a9f1-bc6e-488e-a628-a296b00c3ec3 · outbound
Local Differential Privacy for Deep Learning Concentrated differential privacy: Simplifi- cations, extensions, and lower bounds,
Reference 18
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation da092dc1-46cf-478e-bcd3-b01be99909b2 · outbound
Local Differential Privacy for Deep Learning MVG Mechanism: Differential Privacy under Matrix-Valued Query
Reference 19
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 0e011220-f31d-487b-b6f1-bdd95c33b8b2 · outbound
Local Differential Privacy for Deep Learning Deep learning in neural networks: An overview,
Reference 20
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b2df6e99-1a37-460b-8e31-53cb2c2ab192 · outbound
Local Differential Privacy for Deep Learning Convolutional Neural Networks for Sentence Classification
Reference 21
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3078e9f0-b421-46a8-9f07-ce37796bf447 · outbound
Local Differential Privacy for Deep Learning Imagenet classification with deep convolutional neural networks,
Reference 22
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c7a59605-5cc3-423f-bec1-e13eaa36e622 · outbound
Local Differential Privacy for Deep Learning Finding your way in the fog: Towards a comprehensive definition of fog computing,
Reference 23
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 47fad5d2-925f-43d7-a860-fa48a68af92e · outbound
Local Differential Privacy for Deep Learning An Energy-driven Network Function Virtualization for Multi-domain Software Defined Networks
Reference 24
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 9f03a973-6e80-45be-87c4-cc1d7ce57dba · outbound
Local Differential Privacy for Deep Learning Keras: Deep learning library for theano and tensor- flow,
Reference 25
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 83c07524-0310-438c-a7e4-29f54fa1eb34 · outbound
Local Differential Privacy for Deep Learning Locally differentially private pro- tocols for frequency estimation,
Reference 26
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 3d6944e0-fcff-4595-ba96-3417814a7e6d · outbound
Local Differential Privacy for Deep Learning Gradient-based learning applied to document recognition,
Reference 27
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bba29ccd-1afe-40e1-8d51-50bf8ee03ae2 · outbound
Local Differential Privacy for Deep Learning An efficient and fine-grained big data access control scheme with privacy-preserving policy,
Reference 28
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d06940a0-7678-49c0-bdd9-9cea4c04cd7f · outbound
Local Differential Privacy for Deep Learning Privacy-preserving record linkage for big data: Current approaches and research challenges,
Reference 29
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ad364c58-345d-4cef-9df0-ff0c0c6654b0 · outbound
Local Differential Privacy for Deep Learning A random rotation perturbation approach to privacy preserving data classification,
Reference 30
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 209d478c-0af1-4b8f-a1f1-3b3732491338 · outbound
Local Differential Privacy for Deep Learning Geometric data perturbation for privacy preserving outsourced data mining,
Reference 31
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2adef31c-62f1-48b0-a1cb-6c8e530f58d9 · outbound
Local Differential Privacy for Deep Learning Searchable encryption to reduce encryption degradation in adjustably encrypted databases,
Reference 32
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 3b19d1d4-8051-4ef0-aa23-baa3b369f61b · outbound
Local Differential Privacy for Deep Learning Privacy-aware adaptive data encryption strategy of big data in cloud computing,
Reference 33
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 8426b19c-8d99-4b15-8fc8-9a8b1be1ebb9 · outbound
Local Differential Privacy for Deep Learning Building confidential and efficient query services in the cloud with rasp data perturbation,
Reference 34
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 53ed0775-2e4f-481c-8b01-3f2d15f6865e · outbound
Local Differential Privacy for Deep Learning Efficient data perturbation for privacy preserving and accurate data stream mining,
Reference 35
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 381f09c6-feca-482c-8aea-b65f68d755d4 · outbound
Local Differential Privacy for Deep Learning Designing statistical privacy for your data,
Reference 36
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a5706a7d-5f28-477e-9927-5ae7d72cf782 · outbound
Local Differential Privacy for Deep Learning Efficient privacy preservation of big data for accurate data mining,
Reference 37
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 178f837a-3895-43db-b086-ee4b9e2b90e4 · outbound
Local Differential Privacy for Deep Learning t-closeness: Privacy beyond k-anonymity and l-diversity,
Reference 38
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation e2f20a89-bef2-47be-a5ba-e1211849ea48 · outbound
Local Differential Privacy for Deep Learning Information disclosure under realistic assumptions: Privacy versus optimality,
Reference 39
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b84744ef-f66d-4388-89a2-2fbbc6aad989 · outbound
Local Differential Privacy for Deep Learning Composition attacks and auxiliary information in data privacy,
Reference 40
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ff115aa7-b504-4db7-95db-8bd2406a26e9 · outbound
Local Differential Privacy for Deep Learning Can the utility of anonymized data be used for privacy breaches?
Reference 41
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 00860a87-98b3-4d1e-adf7-bfd20d936ab1 · outbound
Local Differential Privacy for Deep Learning The differential privacy frontier,
Reference 42
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 7060cf78-d278-467e-8dbb-93932a6df031 · outbound
Local Differential Privacy for Deep Learning Differentially private data release for data mining,
Reference 43
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 9cd0f1d9-89c7-4720-81d5-12d368548368 · outbound
Local Differential Privacy for Deep Learning Towards practical differential privacy for sql queries,
Reference 44
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation edc87a47-16ad-44a3-b003-24ee15e9cbe2 · outbound
Local Differential Privacy for Deep Learning Privacy integrated queries: an extensible platform for privacy-preserving data analysis,
Reference 45
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 729e7db0-5371-4174-b7a5-167188aba77e · outbound
Local Differential Privacy for Deep Learning Local, private, efficient protocols for succinct histograms,
Reference 46
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 58d36571-b3eb-4379-adc9-2f8b15e43d0d · outbound
Local Differential Privacy for Deep Learning Multi- key privacy-preserving deep learning in cloud computing,
Reference 47
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 9ab40071-f8bf-4cbf-a762-c1c454236245 · outbound
Local Differential Privacy for Deep Learning Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
Reference 48
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 69cffede-4b3f-42bd-93d7-ac55a60f6bda · outbound
Local Differential Privacy for Deep Learning A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics
Reference 49
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 36460c3b-e8fb-423e-9595-034972b069fe · outbound
Local Differential Privacy for Deep Learning Scikit-learn: Machine learning in python,
Reference 50
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 07314aa1-8189-4d3a-809c-a6f8e6af016b · outbound
Local Differential Privacy for Deep Learning Xgboost: A scalable tree boosting system,
Reference 51
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
No inbound Pith citation observations are available.