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Found 10 papers, 5 papers with code
Tempered Sigmoid Activations for Deep Learning with Differential Privacy
Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer privacy for training data.
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications
We develop techniques to quantify the degree to which a given (training or testing) example is an outlier in the underlying distribution.
That which we call private
Instead, the definitions so named are the basis of refinements and more advanced analyses of the worst-case implications of attackers---without any change assumed in attackers' powers.
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
We study the collection of such statistics in the local differential privacy (LDP) model, and describe an algorithm whose privacy cost is polylogarithmic in the number of changes to a user's value.
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
This paper describes a testing methodology for quantitatively assessing the risk that rare or unique training-data sequences are unintentionally memorized by generative sequence models---a common type of machine-learning model.
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches
The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy.
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
The approach combines, in a black-box fashion, multiple models trained with disjoint datasets, such as records from different subsets of users.
Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries
Techniques based on randomized response enable the collection of potentially sensitive data from clients in a privacy-preserving manner with strong local differential privacy guarantees.
Cryptography and Security
RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees.
Cryptography and Security
