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.

Filter by
Sorted by
Tagged with
1
vote
3answers
74 views

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 ...
0
votes
0answers
20 views

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 (...
2
votes
1answer
62 views

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 ($\...
1
vote
1answer
44 views

How is it possible to define differential privacy on two databases that differ more than a single entry?

The original definition of $\epsilon-$differential privacy is for two databases $D_1$, $D_2$ that differ at most one entry and an randomized algorithm $A$. We have a bound on the probability ratio $\...
3
votes
2answers
54 views

What's the meaning of probabilities in differential privacy formula?

I don't understand what does it mean by "The probability is taken is over the coin tosses of K." Does it mean, the probability distribution is generated based on exactly same data but only the ...
1
vote
0answers
43 views

Differential privacy RAPPOR article proof doubts

Recently I've been trying to understand the RAPPOR proof: \begin{eqnarray*} P(B' = b' | B = b^*) & = & \left(\frac{1}{2}f\right)^{b'_1}\left(1 - \frac{1}{2}f\right)^{1 - b'_1} \times \ldots \\...
2
votes
1answer
63 views

Why does ε-differential privacy protect the subset of 1/ε edges in terms of graphs?

In the book The Algorithmic Foundations of Differential Privacy by Cynthia Dwork, Aaron Roth on page 24, databases that take the form of graphs are discussed. We could on the other hand consider ...
0
votes
0answers
32 views

How to analyse the sensitivity in argmax in exponential mechanism of differential privacy?

Consider we have a database $D=[1,2,1,3]$, and the query for $\mathop{\arg\max}_{i} D_i$. So how to analyse the sentivity of the utility function? for the sensitivity of $u$ equals to $\Delta u=\max_{...
2
votes
2answers
442 views

Why the definition in $\epsilon$-differential privacy is multiplicative rather than additive?

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 ...
1
vote
1answer
137 views

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 ...
2
votes
0answers
64 views

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 ...
3
votes
1answer
91 views

Laplace Mechanism Proof: Why this product operator?

The equation below shows the proof of Laplace mechanism for differential privacy. I am not understanding the product operator, is this a common rule? $$ \frac{p_x(z)}{p_y(z)} = \prod_{i=1}^{k}\left(\...
1
vote
0answers
121 views

Laplace mechanism from Exponential mechanism in Differential Privacy

In McSherry and Talwar's paper which introduces the exponential mechanism for differential privacy, they say that the Laplace mechanism can be captured by choosing the score function as $q(d,r) = - |f(...
3
votes
1answer
286 views

Difference between ε-differential privacy and (ε, δ)-differential privacy

I don't understand the necessity of introducing the additive term δ in the differential privacy definition. Moreover, reading different papers and blogs they say that because of the δ term the ...
3
votes
1answer
283 views

Differential privacy of “randomized responses”

We define randomized responses as follows: In a question that can be responded with a "Yes" or "No", a respondent is asked to flip a fair coin, in secret, and answer the truth if it comes up tails. ...
3
votes
1answer
155 views

what does differential privacy (in machine learning) promise or guarantee?

I am recently reading some papers about privacy-preserving machine learning. Some works incorporate the idea of differential privacy to protect the privacy of the training dataset when the model is ...
3
votes
1answer
105 views

Differential privacy on multiple queries – what is the behavior?

Differential privacy framework still continue to be obscure in the following case: If I make a set of queries, I can join their output to restore the original data. For this issue we have composition ...
2
votes
2answers
64 views

Sensitivity of probability measure in differential privacy

I know that we need some sort of sensitivity(global, local) to calculate noise that needs to be added for differential privacy. The noise is the maximum difference between two neighboring datasets. ...
4
votes
1answer
422 views

Parallel Composition of ($\epsilon, \delta$) differential privacy

I know that if there are $n$ functions $M_1, M_2, \cdots, M_n$ computed on disjoint subsets of the private database whose privacy guarantees are $\epsilon_1, \cdots ,\epsilon_n$ differential privacy, ...
10
votes
1answer
387 views

Intuitive explanation of the $\varepsilon$ parameter in differential privacy

I think I have a decent intuitive understanding of what the $\delta$ parameter means in $(\varepsilon,\delta)$-differential privacy: I can explain it to a non-specialist in terms of "what are the ...
4
votes
1answer
467 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 $D,D'$ ...
3
votes
1answer
191 views

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 ...
2
votes
1answer
272 views

What is ε in differential privacy?

In ε-differential privacy, what does the ε refer to? Is it privacy value or the notation used? Can anyone provide an example of differential privacy?
3
votes
2answers
229 views

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 ...
3
votes
2answers
317 views

Differential privacy definition

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 ...
7
votes
3answers
582 views

Are there any differences between PIR, oblivious transfer and differential privacy?

I am trying to make a taxonomy of the different purposes of some cryptographic protocols. Generally speaking, the purpose of PIR, oblivious transfer and differential privacy--it sounds as if they were ...