# Questions tagged [differential-privacy]

Differential privacy aims to provide means to maximize the accuracy of queries from statistical databases while minimizing the chances of identifying its records.

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### Explaining the reason of radically more accuracy while using different set of hash functions instead of same set of hash functions on some operations

So I am looking for an explanation of an experiment. In this experiment, I took a set of k hash functions. Say the total number of data points I am working on is d. Call an algorithm A which used that ...
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### The sensitivity in differential privacy with deep learning

In differentially private deep learning, the sensitivity is determined by clipping gradient norm(Abadi's paper). In this paper, when the clipping gradient norm is C, the sensitivity is C. Why the ...
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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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### $(\epsilon, \delta)$-differential privacy: main motivation of $\delta$

I am wondering why (not how) we relax $\epsilon$-differential privacy to $(\epsilon, \delta)$-differential privacy. Is the main motivation to reduce the variance of the noise added to the query with a ...
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I have a problem understanding the proof of the corollary of Advanced Composition Theorem Advanced Composition: For all $\varepsilon,\delta,\delta' \geq 0$ the class of $('\varepsilon,\delta)$-...
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### Adding the same noise to all element of a vector is Differential privacy

In Differential privacy, if we add a $N$-dimension private vector with $N$-dimension Laplace or Gauss noise, we obtain differential privacy. However, if we only generate a 1-dimension noise to add it ...
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### Norms in differential privacy

I know that perturbation should be proportional to the $\text{L}_1$-sensitivity of the function if someone wants ($\epsilon,0$)-differential privacy, and proportional to the $\text{L}_2$-sensitivity ...
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### questions about sensitivity in differential privacy

Ted here (What does the term "differential" in "differential privacy" mean?) describes the difference between local and global sensitivity as "By contrast, local and global ...
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### Why does Gausian use less noise than Laplace

Why does Gaussian noise apply less noise than Laplace? has to do maybe that Laplace is been used with queries and each query may have different sensitivity? or maybe because Gaussian is been used on ...
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### What does the term “differential” in “differential privacy” mean?

I'm new in Differential Privacy (DP) and I have two questions: Why do we have the term differential in differential privacy? Are The local and global differential privacy and global and local ...
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### privacy enhancing techniques on image data

To give some context: I am looking for a suite of techniques and tools that can theoretically enable me to conduct analysis such as classification on image datasets in a manner in which a naive ...
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### Differential privacy on medical data

When we apply the differential privacy on medical data to protect the personal data of the patients, how the doctors can access the original data to analyze them and to intervene in real-time. In ...
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### Mechanisms for Locally Private data release for continuous data

Most of the Local Differential Privacy mechanisms I've seen in the literature operate on discrete data, e.g. Randomized Response or RAPPOR. Can you point me to some LDP mechanisms for continuous data (...
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### Interpretation of advanced composition theorem of differential privacy

In "The Algorithmic Foundations of Differential Privacy" book, Advanced Decomposition Theorem (Thm 3.20) is stated as follows: For all "$\epsilon, \delta, \delta' \geq 0$, the class of ...
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### Why the definition in $\epsilon$-differential privacy is multiplicative rather than additiveļ¼

According to its mathematical definition, a random algorithm $M: D\rightarrow R$ satisfies $\epsilon$-differential privacy if the adjacent datasets $x, y \in D$ where $D$ is a whole dataset and ...
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### what is the relationship between epsilon and sensitivity in the Differential-Privacy?

In some Differential-Privacy(DP) papers, they use epsilon as the x-axis in the figures of the experiments' result while other papers use the sensitivity. What is the relationship between epsilon and ...
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### Differential privacy and Shamir's scheme

I have been unable to find any proofs (for my own reference) that demonstrate that Shamir's secret sharing scheme does (or does not) satisfy the definition of $\textbf{differential privacy}$ as ...
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### Differential privacy per record

Generally, differential privacy adds noise to a query result, such as a sum or an average, in an interactive way. Is there any way for implementing differential privacy such that noise will be added ...
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### What is Īµ in differential privacy?

In Īµ-differential privacy, what does the Īµ refer to? Is it privacy value or the notation used? Can anyone provide an example of differential privacy?
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### Differential Privacy and appropriate noise distribution

In differential privacy solutions and more specifically for queries that they do entail counting the proposed solutions define the Laplace distribution that is best calibrated for low error. Other ...
Differential privacy defines "privacy" of a mechanism $A$ as the "closeness" of the two distribution $Pr[A(D) \in S]$ and $Pr[A(D') \in S]$ where $D,D'$ differ in one element. And the distance between ...