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pro vyhledávání: '"Pagh, A."'
In recent years, Gaussian noise has become a popular tool in differentially private algorithms, often replacing Laplace noise which dominated the early literature on differential privacy. Gaussian noise is the standard approach to $\textit{approximat
Externí odkaz:
http://arxiv.org/abs/2408.07021
Autor:
Pagh, Rasmus, Retschmeier, Lukas
Motivated by applications in clustering and synthetic data generation, we consider the problem of releasing a minimum spanning tree (MST) under edge-weight differential privacy constraints where a graph topology $G=(V,E)$ with $n$ vertices and $m$ ed
Externí odkaz:
http://arxiv.org/abs/2408.06997
Autor:
Andersson, Joel Daniel, Henzinger, Monika, Pagh, Rasmus, Steiner, Teresa Anna, Upadhyay, Jalaj
Differential privacy with gradual expiration models the setting where data items arrive in a stream and at a given time $t$ the privacy loss guaranteed for a data item seen at time $(t-d)$ is $\epsilon g(d)$, where $g$ is a monotonically non-decreasi
Externí odkaz:
http://arxiv.org/abs/2406.03802
Autor:
Wu, Hao, Pagh, Rasmus
Given a multiset of $n$ items from $\mathcal{D}$, the \emph{profile reconstruction} problem is to estimate, for $t = 0, 1, \dots, n$, the fraction $\vec{f}[t]$ of items in $\mathcal{D}$ that appear exactly $t$ times. We consider differentially privat
Externí odkaz:
http://arxiv.org/abs/2406.01158
Filter data structures are widely used in various areas of computer science to answer approximate set-membership queries. In many applications, the data grows dynamically, requiring their filters to expand along with the data. However, existing metho
Externí odkaz:
http://arxiv.org/abs/2404.04703
Autor:
Larsen, Kasper Green, Pagh, Rasmus, Persiano, Giuseppe, Pitassi, Toniann, Yeo, Kevin, Zamir, Or
We present a simple and provably optimal non-adaptive cell probe data structure for the static dictionary problem. Our data structure supports storing a set of n key-value pairs from [u]x[u] using s words of space and answering key lookup queries in
Externí odkaz:
http://arxiv.org/abs/2308.16042
Autor:
Andersson, Joel Daniel, Pagh, Rasmus
In privacy under continual observation we study how to release differentially private estimates based on a dataset that evolves over time. The problem of releasing private prefix sums of $x_1,x_2,x_3,\dots \in\{0,1\}$ (where the value of each $x_i$ i
Externí odkaz:
http://arxiv.org/abs/2306.09666
Differentially private mean estimation is an important building block in privacy-preserving algorithms for data analysis and machine learning. Though the trade-off between privacy and utility is well understood in the worst case, many datasets exhibi
Externí odkaz:
http://arxiv.org/abs/2306.08745
Given a collection of vectors $x^{(1)},\dots,x^{(n)} \in \{0,1\}^d$, the selection problem asks to report the index of an "approximately largest" entry in $x=\sum_{j=1}^n x^{(j)}$. Selection abstracts a host of problems--in machine learning it can be
Externí odkaz:
http://arxiv.org/abs/2306.04564
The TeamPlay Project: Analysing and Optimising Time, Energy, and Security for Cyber-Physical Systems
Autor:
Rouxel, Benjamin, Brown, Christopher, Ebeid, Emad, Eder, Kerstin, Falk, Heiko, Grelck, Clemens, Holst, Jesper, Jadhav, Shashank, Marquer, Yoann, De Alejandro, Marcos Martinez, Nikov, Kris, Sahafi, Ali, Lundquist, Ulrik Pagh Schultz, Seewald, Adam, Vassalos, Vangelis, Wegener, Simon, Zendra, Olivier
Publikováno v:
Design, Automation and Test in Europe, Apr 2023, Antwerp, Belgium
Non-functional properties, such as energy, time, and security (ETS) are becoming increasingly important in Cyber-Physical Systems (CPS) programming. This article describes TeamPlay, a research project funded under the EU Horizon 2020 programme betwee
Externí odkaz:
http://arxiv.org/abs/2306.06115