Questions tagged [differential-privacy]

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Differential privacy [closed]

I have a dataset containing private information and before making it available for data scientist team i want to use differential privacy over it as to mask the private information. It is more likely ...
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What data is sent from a client to a server in a federated learning setting?

So far, I thought federated learning works like this: All clients have the same machine learning model (if not personalized). They have their unique data and then train this model (e.g., neural ...
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Can we get the input from a multilayer perceptron based on the output of one of its hidden layers?

I was reading a relatively new paper that proposed to split a nerual networks layers into groups and sending each group to different nodes to train them in a distributed manner. In order to not send ...
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Moments accountant beyond subsampled Gaussian mechanism

Moments accountant has been in the first place applied on the subsampled Gaussian mechanism, leading to tight privacy cost estimation and efficient differentially private SGD-based learning in neural ...
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The score function q(d,r) in the exponential mechanism

Are there any examples (other than the auction problem) to show how one should set and compute the score function q(d,r) in the exponential mechanism.
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Differentially Private Empirical Risk Minimization – How to Generate Noise Vector?

The output perturbation technique for differentially private empirical risk minimization (Chaudhuri et al., 2011) involves the introduction of a noise vector $b\in\mathbb{R}^d$ with density function $$...
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Local sensitivity for max and mean?

Using local sensitivity to sanitize max and mean? For e.g., (Sensitivity = Max_value - Second_Max_value) in a dataset? What are the disadvantages of this approach? The global sensitivity is unbounded....
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2answers
112 views

What is the difference between data perturbation and differential privacy?

I cannot distinguish the terms "data perturbation" and "differential privacy". If the data perturbation is the process that adds some small value sampled from specific distributions such as Laplacian ...
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1answer
124 views

Global sensitivity of mean and variance in differential privacy?

Please explain me why global sensitivity of a mean or variance queries will be (b-a)/n and (b-a)^2/n where b is the upper ...
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25 views

Some questions regarding implementation of smooth sensitivity in differential privacy

I am currently trying to implement the mechanism of adding noise based on smooth sensitivity [1] (instead of the more common Laplace mechanism which is based on global sensitivity). I want to output a ...
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1answer
37 views

Communicating aggregate percentage changes in data without exposing individual contributors

So i have a dataset that tracks widget production from 100 different factories, each individually owned and highly competitive. Each line contains the factory name, the date of production, and the # ...
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Is differential privacy applicable to summation value? [closed]

Given a collection of real data, we want to do some statistical analysis. For example, we take sum of them and reveal it to someone else. But, we want to guarantee some sort of privacy of ...
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1answer
59 views

How can one apply differential privacy to this network dataset? [closed]

I've been reading up recently on differential privacy and I'm just starting to understand it. I've also read this paper that basically determined the sexual orientation of a user using Facebook ...
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142 views

differentially private release of histograms (non-negative valued queries)

Two practical questions arise when releasing differentially private histograms/counts via addition of Laplace/Gaussian noise: 1) Is the result of noise addition truncated/rounded (since we know that ...
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1answer
332 views

How to generate a sanitized dataset using Differential privacy?

I'm learning about differential privacy. I understand the concept behind differential privacy, that you can add a small noise to the query to mask the true value using transformations like Laplace or ...
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1answer
632 views

What are global sensitivity and local sensitivity in differential privacy?

I am learning differential privacy now, and there is no one surrounding I can ask questions about differential privacy. I am confused about the definitions of the global sensitivity and local ...
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1answer
43 views

postprocessing additive noise in differentially private data

when releasing differentially private datasets we often have (or can plausibly assume) knowledge of the noise added to the data to achieve privacy - we can even have good approximations of the scale ...
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1answer
100 views

Why does “sticky noise” defy averaging attack?

I have read an interesting paper (pdf) describing how a privacy preserving technique might be breached, but I am having trouble understanding the following paragraph describing one of several layers ...
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1answer
150 views

How to test for differential privacy on multiple choice data?

I apologize I am new to statistics so I do not know all terms and concepts. My current algorithm for adding noise to multiple-choice favorite color data is this: ...
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1answer
144 views

Most powerful test bounds in differential privacy setting

I am interested in the setting of differential privacy- let's say a random function $\mathcal{D}:X\to\mathbb{R}$ discriminates between (distinct) $x, y \in X$ in a differentially private way if $$ \...
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1answer
96 views

Issue with the definition of differential privacy

The definition of differential privacy states that if $\mathcal{M}$ is $(\epsilon,\delta)$-differentially private, then $\forall x,y$ such that $||x-y||_1\leq1$ and for all $S \subseteq \mathrm{Range}...
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2answers
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Privacy through moving averages?

I am considering the following hypothetical situation: I have a time series of data. In general, 'the public' should have access to features of this data. However, making the time series available ...
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2answers
124 views

Differential privacy of identity query

I am trying to understand some of the papers that present identity query mechanisms that satisfies differential privacy, for example the compressive mechanism which uses what they call a universal ...
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3answers
153 views

Effect of exp(ϵ) in Differential Privacy Definition

I am reading about differential privacy and would like to understand the implications of the different values of $\varepsilon$ in the definition below: $$\mathbb{P}[K(D_1) \in \mathcal{S}] \leqslant \...
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1answer
140 views

Differential Privacy: why $\delta$ negligible on the row numbers?

The definition of differential privacy says that an algorithm $M$ is $(\epsilon,\delta)$-differentially private if $$P(M(x \in D) \in S)\leq e^\epsilon P(M(x \in D')\in S) + \delta$$ where $D,D'$ ...
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2answers
6k views

What is meant by “Laplace noise”?

I am currently writing algorithm for differential privacy using the Laplace mechanism. Unfortunately I have no background in statistics, therefore a lot of terms are unknown to me. So now I'm ...
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4answers
873 views

Has the journal Science endorsed the Garden of Forking Pathes Analyses?

The idea of adaptive data analysis is that you alter your plan for analyzing the data as you learn more about it. In the case of exploratory data analysis (EDA), this is generally a good idea (you are ...
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1answer
1k views

What is the purpose of using a Laplacian distribution in adding noise for Differential Privacy?

I am reading up on Differential Privacy and it is mentioned that the technique relies on adding some controlled noise to the release of responses to queries towards a statistical database. This is ...
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What current methods for statistical disclosure limitation are best trade-offs between data privacy and data utility?

In many situations raw microdata is not released by institutions due to privacy preserving. Many techniques are used for protecting sensitive values in data. But many of them can destroy multivariate ...