Questions tagged [differential-privacy]

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How do I find the global sensitivity of a dataset

Below is my dataset, that I have to find the global sensitivity for: I am thinking of finding the difference between all the grades and then finding it with the formula: GS(f)=maxx,x′:d(x,x′)<=1|f(...
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Does a differentially private algorithm allow to distinguish datasets of the same size that differ in one entry?

The definition of differential privacy states that a (learning) algorithm $A$ mapping a dataset $D\subset \mathcal{X}$ to a model $\theta\in\Theta$ is differential private if $$e^{-\epsilon} \leq \...
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Verifying epsilon in differential privacy [closed]

Suppose I create a synthetic dataset out of a real dataset using some epsilon. How can one verify that my synthetic dataset is atleast epsilon differential private?
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How should one calculate the confidence interval in 2020 census data, which uses differential privacy?

In 2020, the U.S. Census Bureau began injecting noise into census counts using a differential privacy technique. See here for a popular press description and here for some official literature. The ...
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Differential Privacy on tabular data

I wanted to create a deferentially private dataset of my private dataset. While I understand the concepts behind DP, I am unable to grasp how this can be applied to these fields. e.g. ...
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Should data be anonymized irrespective of the domain? [closed]

I am using a dataset from Marketing and sales department. The dataset contains customer name (company name), company address, pincode, no of orders placed, revenue generated from that customer etc. My ...
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Why is DP-SGD differentially private?

The paper 'Deep Learning with Differential Privacy' explains how to make a deep learning algorithm as differentially private. This explanation is implemented in Tensorflow Privacy My question is: we ...
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adding noise to prediction task

Say that a teacher wishes to use a standard prediction task from Kaggle as a course assignment, and the idea is to have students submit their predictions, and award grades based on a test set (...
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Why does Chebyshev's inequality yield that the probability of Laplacian noise being bigger than x is bounded like this?

I am trying to understand this proof of the bounds of Laplacian noise used in a paper on differential privacy. Given a random variable $Lap\left ( \frac{\Delta f}{\varepsilon } \right )$, apparently ...
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Adding Laplace noise to a learned neural network

My question is related to the concept of differential privacy and deep learning. I found many papers to learn neural networks with differential privacy, but is it also possible to achieve differential ...
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Does $\epsilon$-differential privacy treat databases with one record of difference a completely different databases?

Does $\epsilon$-differential privacy treat databases with one record difference completely different database? What I want to know is about continuous release. Suppose we have a set of users and some ...
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Can a folded LaPlace distribution (or other folded distributions) be used with Ɛ-differential privacy

I have a single value in (or over) our dataset, let's say a count of something, and we want to keep that value private within a certain range. This range is the sensitivity. The adversary can ask if a ...
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Upper Bound and Lower Bound on Means when Distributions are bounded?

Suppose we have two different probability distributions $p, q$ defined on input $x \in [0,1]$. We know that for any value of $x$ in the domain, we have $\exp^{-a} \leq \frac{p(x)}{q(x)} \leq \exp^{a} $...
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Laplace Inequality

I am trying to prove that if $r_i \sim Lap(0,1/\varepsilon)$ where $\varepsilon >0$ then: $$Pr[r_i \geq 1+r^*] \geq e^{-\varepsilon}Pr[r_i \geq r^{*}]$$. I know that for $r*>0$ it satisfies ...
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What is statistical difference?

In the paper "Calibrating Noise to Sensitivity in Private Data Analysis" by Dwork et al., the term "statistical difference" is used as following (in page 280): Finally, if a $1 − \...
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Laplace mechanism on vector record?

Does the definition of neighboring database in differential privacy capture the multi-dimensional record? Let's say we have a database domain $\mathbb{N}^{n\times d}$ where $n$ is the number of ...
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Lemma KL-Divergence (Differential Privacy)

I am studying differential privacy and I got stuck again in proof of a lemma. Which is: "$D_{\infty}^\delta(Y||Z) \leq \epsilon$ if and only if there exists a random variable $Y'$ such that $\...
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Interpretation of the ratio of two pdfs evaluated at a certain point?

What is the interpretation of the ratio of two pdfs evaluated at a certain point? Is that a statistical distance? Is there any applications of it? I only know one application of differential privacy, ...
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Does Laplace mechanism work only on linear query?

Does Laplace mechanism work only on non-multiplicative query? For example, suppose a database (an array here) $\mathbf{x} = (x_1, \ldots, x_n)$. Is it possible to do design laplacian mechanism for ...
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Why $Pr[X-\mu \geq t]= Pr[e^{\lambda(X-\mu)} \geq e^{\lambda t}]$ for all $\lambda> 0$

I hope everyone is having a nice day. I don't know why this inequality holds. $$ Pr[X-\mu \geq t]= Pr[e^{\lambda(X-\mu)} \geq e^{\lambda t}] $$ For $\lambda >0$. I guess it has something to do ...
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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1 answer
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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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12 votes
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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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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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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 ...
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