Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy
<|reference_start|>Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy: In the task of differentially private (DP) continual counting, we receive a stream of increments and our goal is to output an approximate running total of these increments, without revealing too much about any specific increment. Despite its simplicity, differentially private continual counting has attracted significant attention both in theory and in practice. Existing algorithms for differentially private continual counting are either inefficient in terms of their space usage or add an excessive amount of noise, inducing suboptimal utility.   The most practical DP continual counting algorithms add carefully correlated Gaussian noise to the values. The task of choosing the covariance for this noise can be expressed in terms of factoring the lower-triangular matrix of ones (which computes prefix sums). We present two approaches from this class (for different parameter regimes) that achieve near-optimal utility for DP continual counting and only require logarithmic or polylogarithmic space (and time).   Our first approach is based on a space-efficient streaming matrix multiplication algorithm for a class of Toeplitz matrices. We show that to instantiate this algorithm for DP continual counting, it is sufficient to find a low-degree rational function that approximates the square root on a circle in the complex plane. We then apply and extend tools from approximation theory to achieve this. We also derive efficient closed-forms for the objective function for arbitrarily many steps, and show direct numerical optimization yields a highly practical solution to the problem. Our second approach combines our first approach with a recursive construction similar to the binary tree mechanism.<|reference_end|>
@article{dvijotham2024efficient,
    title={Efficient and Near-Optimal Noise Generation for Streaming Differential
  Privacy},
    author={Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas
  Steinke, Abhradeep Thakurta},
    journal={arXiv preprint arXiv:2404.16706},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16706},
    primaryClass={cs.DS cs.CC cs.CR cs.LG}
}