105 citations · 132 across the 22 of their papers we have counts for
6 papers · 1 filter
Differentially Private Fine-tuning of Language Models
Da Yu, Saurabh Naik, Arturs Backurs +9
We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-scale pre-trained language models, which achieve the state-of-the-art privacy versus…
Differentially Private n-gram Extraction
Kunho Kim, Sivakanth Gopi, Janardhan Kulkarni +1
We revisit the problem of -gram extraction in the differential privacy setting. In this problem, given a corpus of private text data, the goal is to release as many -grams as…
Lower Bounds for Maximally Recoverable Tensor Code and Higher Order MDS Codes
Joshua Brakensiek, Sivakanth Gopi, Visu Makam
An -tensor code consists of matrices whose columns satisfy `' parity checks and rows satisfy `' parity checks (i.e., a tensor code is the tensor produc…
Trellis BMA: Coded Trace Reconstruction on IDS Channels for DNA Storage
Sundara Rajan Srinivasavaradhan, Sivakanth Gopi, Henry D. Pfister +1
Sequencing a DNA strand, as part of the read process in DNA storage, produces multiple noisy copies which can be combined to produce better estimates of the original strand; this i…
Numerical Composition of Differential Privacy
Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
We give a fast algorithm to optimally compose privacy guarantees of differentially private (DP) algorithms to arbitrary accuracy. Our method is based on the notion of privacy loss…
Fast and Memory Efficient Differentially Private-SGD via JL Projections
Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni +3
Differentially Private-SGD (DP-SGD) of Abadi et al. (2016) and its variations are the only known algorithms for private training of large scale neural networks. This algorithm requ…