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DP-cryptography
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/11681878_14openalex-snapshot
Calibrating Noise to Sensitivity in Private Data Analysis
inferredcites →https://doi.org/10.1007/978-3-030-26951-7_22openalex-snapshot
The Privacy Blanket of the Shuffle Model
inferredcites →https://doi.org/10.1007/978-3-030-45724-2_27openalex-snapshot
Private Aggregation from Fewer Anonymous Messages
inferredcites →https://doi.org/10.1007/978-3-642-03356-8_8openalex-snapshot
Computational Differential Privacy
inferredcites →https://doi.org/10.1007/978-3-642-36594-2_26openalex-snapshot
Analyzing Graphs with Node Differential Privacy
inferredcites →https://doi.org/10.1007/978-3-662-43948-7openalex-snapshot
Automata, Languages, and Programming
inferredcites →https://doi.org/10.1137/1.9781611975482.151openalex-snapshot
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
inferredcites →https://doi.org/10.1145/3187009.3177733openalex-snapshot
Towards practical differential privacy for SQL queries
inferredcites →https://doi.org/10.1145/3287287openalex-snapshot
Understanding database reconstruction attacks on public data
inferredcites →https://doi.org/10.1145/358549.358563openalex-snapshot
Untraceable electronic mail, return addresses, and digital pseudonyms
inferredcites →https://doi.org/10.1145/773153.773173openalex-snapshot
Revealing information while preserving privacy
inferredcites →https://doi.org/10.14722/ndss.2016.23279openalex-snapshot
Dependence Makes You Vulnerable: Differential Privacy Under Dependent Tuples
inferredcites →https://doi.org/10.14778/3231751.3231769openalex-snapshot
Optimizing error of high-dimensional statistical queries under differential privacy
inferredcites →https://doi.org/10.1515/popets-2018-0032openalex-snapshot
Differentially Private Oblivious RAM
inferredcites →https://doi.org/10.1561/9781601988195openalex-snapshot
The Algorithmic Foundations of Differential Privacy
inferredcites →https://doi.org/10.2478/popets-2019-0034openalex-snapshot
Cheaper Private Set Intersection via Differentially Private Leakage
inferredcites →https://doi.org/10.29012/jpc.v6i1.634openalex-snapshot
On the 'Semantics' of Differential Privacy: A Bayesian Formulation
inferredcites →https://doi.org/10.48550/arxiv.1712.01524openalex-snapshot
Collecting Telemetry Data Privately
inferredcites →https://doi.org/10.48550/arxiv.1807.01647openalex-snapshot
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences
inferredcites →https://doi.org/10.5555/2387880.2387895openalex-snapshot
DJoin: differentially private join queries over distributed databases
inferredcites →https://doi.org/10.5555/2387880.2387898openalex-snapshot
Dissent in numbers: making strong anonymity scale
inferredcites →https://doi.org/10.5555/3291168.3291221openalex-snapshot
Karaoke: distributed private messaging immune to passive traffic analysis
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4724820openalex-extracted
Homomorphic Encrypted Revenue Management
inferred← citeshttps://doi.org/10.1002/cpe.7565openalex-snapshot
Differential privacy: Review of improving utility through cryptography‐based technologies
inferred← citeshttps://doi.org/10.1007/978-3-031-26412-2_34openalex-snapshot
Marginal Release Under Multi-party Personalized Differential Privacy
inferred← citeshttps://doi.org/10.1007/978-3-031-30637-2_20openalex-snapshot
Accurate and Efficient Trajectory-Based Contact Tracing with Secure Computation and Geo-Indistinguishability
inferred← citeshttps://doi.org/10.1007/978-3-031-58923-2_9openalex-snapshot
Privacy in Federated Learning Natural Language Models
inferred← citeshttps://doi.org/10.1007/978-3-032-38695-3_13openalex-snapshot
TRIP: Thresholding in Regression with Input Privacy
inferred← citeshttps://doi.org/10.1007/978-981-95-3551-4_7openalex-snapshot
A Novel Dual-Layer Access Control Model Based on Blockchain for the Case-Involved Property Management
inferred← citeshttps://doi.org/10.1007/978-981-95-5787-5_6openalex-snapshot
Hybrid Differential Privacy
inferred← citeshttps://doi.org/10.1007/s44443-025-00028-zopenalex-snapshot
A verifiable scheme for differential privacy based on zero-knowledge proofs
inferred← citeshttps://doi.org/10.1016/j.commatsci.2019.04.014openalex-snapshot
Synergistic approach to quantifying information on a crack-based network in loess/water material composites using deep learning and network science
inferred← citeshttps://doi.org/10.1016/j.geoai.2025.100004openalex-snapshot
Differential Privacy in Geotechnical Engineering
inferred← citeshttps://doi.org/10.1016/j.procs.2024.10.152openalex-snapshot
Secure Multi-party Key-Value Data Statistics Against Malicious Models
inferred← citeshttps://doi.org/10.1063/5.0321997openalex-snapshot
Distributed selective training of autoencoders
inferred← citeshttps://doi.org/10.1109/bigdata55660.2022.10020247openalex-snapshot
User-Entity Differential Privacy in Learning Natural Language Models
inferred← citeshttps://doi.org/10.1109/infocom48880.2022.9796975openalex-snapshot
When Deep Learning Meets Steganography: Protecting Inference Privacy in the Dark
inferred← citeshttps://doi.org/10.1109/satml68715.2026.00057openalex-snapshot
SoK: Enhancing Cryptographic Collaborative Learning with Differential Privacy
inferred← citeshttps://doi.org/10.1109/sp46215.2023.10179422openalex-snapshot
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy
inferred← citeshttps://doi.org/10.1109/sp54263.2024.00063openalex-snapshot
Orca: FSS-based Secure Training and Inference with GPUs
inferred← citeshttps://doi.org/10.1109/tdsc.2022.3227568openalex-snapshot
Efficient Noise Generation Protocols for Differentially Private Multiparty Computation
inferred← citeshttps://doi.org/10.1109/tmc.2022.3223578openalex-snapshot
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps
inferred← citeshttps://doi.org/10.1109/tps-isa58951.2023.00024openalex-snapshot
Centering Policy and Practice: Research Gaps Around Usable Differential Privacy
inferred← citeshttps://doi.org/10.1145/3490237openalex-snapshot
A Survey on Differential Privacy for Unstructured Data Content
inferred← citeshttps://doi.org/10.1145/3548606.3559383openalex-snapshot
Distributed, Private, Sparse Histograms in the Two-Server Model
inferred← citeshttps://doi.org/10.1145/3548606.3560610openalex-snapshot
Strengthening Order Preserving Encryption with Differential Privacy
inferred← citeshttps://doi.org/10.1145/3605759.3625258openalex-snapshot
A Probabilistic Design for Practical Homomorphic Majority Voting with Intrinsic Differential Privacy
inferred← citeshttps://doi.org/10.1145/3650028openalex-snapshot
Belt and Braces: When Federated Learning Meets Differential Privacy
inferred← citeshttps://doi.org/10.1145/3651153openalex-snapshot
Scenario-based Adaptations of Differential Privacy: A Technical Survey
inferred← citeshttps://doi.org/10.14778/3717755.3717764openalex-snapshot
SPECIAL: SynoPsis AssistEd Secure Collaborative AnaLytics
inferred← citeshttps://doi.org/10.3390/app14031224openalex-snapshot
Privacy-Preserving Deep Learning Framework Based on Restricted Boltzmann Machines and Instance Reduction Algorithms
inferred← citeshttps://doi.org/10.48550/arxiv.2011.00976openalex-snapshot
Improving Utility of Differentially Private Mechanisms through Cryptography-based Technologies: a Survey
inferred← citeshttps://doi.org/10.48550/arxiv.2107.12407openalex-snapshot
Selective MPC: Distributed Computation of Differentially Private Key-Value Statistics
inferred← citeshttps://doi.org/10.48550/arxiv.2108.04417openalex-snapshot
Privacy-Preserving Machine Learning: Methods, Challenges and Directions
inferred← citeshttps://doi.org/10.48550/arxiv.2110.06559openalex-snapshot
Infinitely Divisible Noise in the Low Privacy Regime
inferred← citeshttps://doi.org/10.5023/jappstat.54.113openalex-snapshot
An Introduction to Differential Privacy: Privacy Properties and Models
inferred← citeshttps://doi.org/10.70675/10bb5f06zd04az4edbzadfcz7fabd892f109openalex-snapshot
Combining differential privacy and homomorphic encryption for privacy-preserving collaborative machine learning
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