59 citations · 162 across the 5 of their papers we have counts for
5 papers
Privacy Amplification via Random Check-Ins
Borja Balle, Peter Kairouz, H. Brendan McMahan +2
Differentially Private Stochastic Gradient Descent (DP-SGD) forms a fundamental building block in many applications for learning over sensitive data. Two standard approaches, priva…
Tempered Sigmoid Activations for Deep Learning with Differential Privacy
Nicolas Papernot, Abhradeep Thakurta, Shuang Song +2
Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer privacy for training data. In practice, this has been mostly an aftertho…
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov +4
Recently, a number of approaches and techniques have been introduced for reporting software statistics with strong privacy guarantees. These range from abstract algorithms to compr…
Practical Locally Private Heavy Hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer +1
We present new practical local differentially private heavy hitters algorithms achieving optimal or near-optimal worst-case error and running time -- TreeHist and Bitstogram. In bo…
Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds
Raef Bassily, Adam Smith, Abhradeep Thakurta
In this paper, we initiate a systematic investigation of differentially private algorithms for convex empirical risk minimization. Various instantiations of this problem have been…