Asaresult, sinceeach 6 Table 1: results (", )-DPwith = 10 5. The? indicates representations.

Releasing seemingly innocuous functions of a data set can easily compromise the privacy of individuals, whether the functions are simple counts [35]orcomplexmachine learning models like deep neural networks [52,30].

In this paper, we focus on conducting iterative methods like DP-SGD in the setting of federated learning (FL) wherein the data is distributed among many devices(clients).

Private data analysis suffers acostly curse of dimensionality.

We disprove this conjecture and show,fortwonotions ofalgorithmic stability (including uniform stability), that the stability of Nesterov's accelerated method in fact deteriorates exponentiallyfast withthenumberofgradientsteps.

We introduce a new method for releasing answers to statistical queries with differential privacy, based on the Johnson-Lindenstrauss lemma. The key idea is to randomly project the query answers to a lower dimensional space so that the distance between any two vectors of feasible query answers is preserved up to an additive error. Then we answer the projected queries using a simple noise-adding mechanism, and lift the answers up to the original dimension. Using this method, we give, for the first time, purely differentially private mechanisms with optimal worst case sample complexity under average error for answering a workload of $k$ queries over a universe of size $N$. As other applications, we give the first purely private efficient mechanisms with optimal sample complexity for computing the covariance of a bounded high-dimensional distribution, and for answering 2-way marginal queries. We also show that, up to the dependence on the error, a variant of our mechanism is nearly optimal for every given query workload.

Few leaders would dispute the fact that business today is driven by data and smart algorithms. Yet, rather than real digital transformation, many instead pursue digital incrementalism, using automation to cut costs or, worse -- cut jobs. Doing so might buy you some time from impatient shareholders, but it will be short-lived unless you can face the challenge: How do you reimagine what you do for a new era of AI-powered competition? The high unemployment numbers of the Covid-19 recession have obscured a systemic problem: the accelerating effect of automation on the workforce. We have been here before.

Preserving differential privacy during empirical risk minimization model training has been extensively studied under centralized and sample-wise distributed dataset settings. This paper considers a nearly unexplored context with features partitioned among different parties under privacy restriction. Motivated by the nearly optimal utility guarantee achieved by centralized private Frank-Wolfe algorithm (Talwar, Thakurta, and Zhang 2015), we develop a distributed variant with guaranteed privacy, utility and uplink communication complexity. To obtain these guarantees, we provide a much generalized convergence analysis for block-coordinate Frank-Wolfe under arbitrary sampling, which greatly extends known convergence results that are only applicable to two specific block sampling distributions. We also design an active feature sharing scheme by utilizing private Johnson-Lindenstrauss transform, which is the key to updating local partial gradients in a differentially private and communication efficient manner.

"It was a bright cold day in April, and the clocks were striking thirteen." The opening line of George Orwell's great novel 1984 sets up a cautionary tale of how the future could look. In its way, what we're confronting in 2020 is every bit as chilling, but it is also an exciting time of opportunity. Yet, just as the clocks "were striking thirteen", there could be alarming changes at work in the name of progress. "Within the next five years, 20 per cent of all the jobs that exist today will have been automated away," claims futurologist Rohit Talwar.