Questions tagged [differential-privacy]
Questions about differential privacy, a measure and method for preserving privacy when querying statistical datasets.
7
questions
1
vote
0
answers
23
views
Upper bound via standard manipulation in proof of semi-private learning
I have been reading a paper on private learning [1].
In the proof of lemma 3.3. they claim that
$$
2\left(\frac{2e n_\text{pub}}{d}\right)^{2d}e^{-\alpha n_\text{pub}/4}
$$
is upper bounded by $\beta$ ...
1
vote
1
answer
87
views
Histogram representation of a database
I'm reading though The Algorithmic Foundations
of Differential Privacy and the authors define a database in a mathematically convenient way. Unfortunately I'm a little confused about what the ...
1
vote
1
answer
110
views
What is the sensitivity of gradient clipping in Differentially Private SGD?
As far as I understand, the Gaussian Mechanism uses the sensitivity of the input function to determine the right amount of noise. In DP-SGD, said input function is taking the gradient and clipping it ...
3
votes
1
answer
201
views
Is the differential privacy definition with lower and upper bound equivalent to the definition with just an upper bound?
According to Wikipedia, given a randomized algorithm $\mathcal{A}$, two neighboring datasets $D_1, D_2$, a real number $\epsilon > 0$, $\mathcal{A}$ provides $\epsilon$-differential privacy, if
$$
\...
1
vote
0
answers
94
views
Differential Privacy with only positive noise
The Laplace mechanism is a standard way of making the output of a function $f$ differentially private.
More concretely, let $\Delta_f$ be the sensitivity of $f$, i.e. the maximum value by which the ...
2
votes
0
answers
55
views
In a machine learning system, why use differentially private SGD if our input data is already perturbed by a DP mechanism?
I'm trying to implement my own version of a deep neural network with differential privacy to preserve the privacy of the parties involved in the training dataset.
I'm using the method by Abadi et al. ...
2
votes
1
answer
92
views
About the definition of "differential privacy" in communication complexity
In the context of communication complexity I see a definition of differential privacy which isn't totally clear to me as to why it makes sense.
So the two parties $A$ and $B$ draw two strings $X$ ...