# Questions tagged [differential-privacy]

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1answer
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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 (...
1answer
46 views

### 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 ...
0answers
42 views

### 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 ...
0answers
12 views

### The amount of noise needed for independent vs. dependent features in differential privacy

There is this paper that says: Remark 1. DP noise is applied with the assumption that features are independent from each other, meaning a maximal amount of noise must be applied to each feature to ...
0answers
19 views

### How to create differentially private synthetic or noisy dataset?

I can view my original dataset as points in n dimensional space, how do I create a differentially private synthetic dataset for such a dataset. You can also think of it as having a dataset with m ...
1answer
19 views

### 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 ...
0answers
8 views

### How could I implement data binning for differential privacy, similar to Apple's Count Mean Sketch or Hadamard Count Mean Sketch?

I'm looking at the Apple differential privacy document here and it has the paragraph: The noise injection step works as follows: After encoding the input as a vector using a hash function, each ...
1answer
30 views

### 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 ...
1answer
44 views

### 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 $\exp^{-a} \leq \frac{p(x)}{q(x)} \leq \exp^{a}$...
1answer
41 views

### 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 ...
0answers
28 views

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30 views

### 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, ...
1answer
80 views

### 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 ...
1answer
23 views

### 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 ...
0answers
30 views

### 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 ...
0answers
13 views

### 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 ...
3answers
303 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 ...
1answer
439 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 ...
1answer
38 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 # ...
0answers
50 views

### 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 ...
1answer
75 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 ...
2answers
320 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 ...
1answer
513 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 ...
2answers
1k 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 ...
1answer
48 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 ...
1answer
111 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 ...
1answer
213 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: ...
1answer
184 views

1answer
177 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 $D,D'$ ...
2answers
7k 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 ...
4answers
902 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 ...
1answer
2k 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 ...
0answers
66 views

### 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 ...