DP - Related Paper

survey

• [ ] Signal Processing and Machine Learning with Differential Privacy
• [ ]

($\epsilon,\delta$)-differential privacy

• [ ] Our Data, Ourselves: Privacy Via Distributed Noise Generation

$\delta$-probability privacy

• [ ] Privacy: Theory meets practice on the map

Sensitivity

• [ ] Calibrating Noise to Sensitivity in Private Data Analysis
• [ ]

Composition

• [ ] Boosting and Differential Privacy
• [ ] Interactive Privacy via the Median Mechanism
• [ ] [Privacy odometers and filters: Pay-as-you-go composition]
• [ ] [Differential privacy and robust statistics]

Mechanism

• [ ] Differentially Private Empirical Risk Minimization with Input Perturbation
• [ ]

Location DP

• [ ] A Privacy Preserving Framework for Worker’s Location in Spatial Crowdsourcing Based on Local Differential Privacy
• [ ] [Differentially Private Publication of Location Entropy]

Distributed Lap

• [ ] Our Data, Ourselves: Privacy Via Distributed Noise Generation
• [ ] Universally Utility-Maximizing Privacy Mechanisms

DP in SGD

• [ ] [Private empirical risk minimization: Efficient algorithms and tight error bounds]
• [ ] [Stochastic gradient descent with differentially private updates]

DP in Machine Learning

• [ ] [Analyze Gauss: Optimal bounds for privacy-preserving principal component analysis]
• [ ] Related work in [2015-Reza Shokri] Privacy-Preserving Deep Learning

Error Bound in DP

• [ ] [Concentrated differential privacy: Simplifications, extensions, and lower bounds.]

• [ ] [R’enyi differential privacy]