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The nonlocal behavior of quantum mechanics can be used to generate guaranteed fresh randomness from an untrusted device that consists of two nonsignalling components; since the generation process requires some initial fresh randomness to…

Quantum Physics · Physics 2013-02-26 Serge Fehr , Ran Gelles , Christian Schaffner

The randomized power method has gained significant interest due to its simplicity and efficient handling of large-scale spectral analysis and recommendation tasks. However, its application to large datasets containing personal information…

Machine Learning · Computer Science 2025-06-13 Julien Nicolas , César Sabater , Mohamed Maouche , Sonia Ben Mokhtar , Mark Coates

Current approaches to novelty or anomaly detection are based on deep neural networks. Despite their effectiveness, neural networks are also vulnerable to imperceptible deformations of the input data. This is a serious issue in critical…

Computer Vision and Pattern Recognition · Computer Science 2023-06-07 Ranya Almohsen , Shivang Patel , Donald A. Adjeroh , Gianfranco Doretto

The unpredictability of random numbers is fundamental to both digital security and applications that fairly distribute resources. However, existing random number generators have limitations-the generation processes cannot be fully traced,…

We consider a cascade network where a sequence of nodes each send a message to their downstream neighbor to enable coordination, the first node having access to an information signal. An adversary also receives all of the communication as…

Information Theory · Computer Science 2014-11-04 Paul Cuff

The prime numbers look like a randomly chosen sequence of natural numbers, but there is still no strict theory to determine 'Randomness'. In these years, cryptography has developed a battery of statistical tests for randomness. In this…

Number Theory · Mathematics 2011-02-19 Wang Liang , Huang Yan

Estimating causal effects from randomized experiments is only possible if participants are willing to disclose their potentially sensitive responses. Differential privacy, a widely used framework for ensuring an algorithms privacy…

Machine Learning · Statistics 2025-05-29 Adel Javanmard , Vahab Mirrokni , Jean Pouget-Abadie

We define a variation on the well-known problem of private message transmission. This new problem called private randomness agreement (PRA) gives two participants access to a public, authenticated channel alongside the main channels, and…

Information Theory · Computer Science 2023-02-15 René Bødker Christensen , Petar Popovski

A missing piece in quantum information theory, with very few exceptions, has been to provide the random coding exponents for quantum information-processing protocols. We remedy the situation by providing these exponents for a variety of…

Quantum Physics · Physics 2015-09-30 Naresh Sharma

Cryptographic protocols are often based on the two main resources: private randomness and private key. In this paper, we develop a relationship between these two resources. First, we show that any state containing perfect, directly…

Quantum Physics · Physics 2020-07-21 Karol Horodecki , Ryszard P. Kostecki , Roberto Salazar , Michał Studziński

Most online lotteries today fail to ensure the verifiability of the random process and rely on a trusted third party. This issue has received little attention since the emergence of distributed protocols like Bitcoin that demonstrated the…

Distributed, Parallel, and Cluster Computing · Computer Science 2017-09-18 Stéphane Grumbach , Robert Riemann

Steganographic protocols enable one to embed covert messages into inconspicuous data over a public communication channel in such a way that no one, aside from the sender and the intended receiver, can even detect the presence of the secret…

Cryptography and Security · Computer Science 2012-02-06 Aggelos Kiayias , Alexander Russell , Narasimha Shashidhar

The approximate joint diagonalization of a set of matrices consists in finding a basis in which these matrices are as diagonal as possible. This problem naturally appears in several statistical learning tasks such as blind signal…

Numerical Analysis · Computer Science 2018-12-03 Pierre Ablin , Jean-François Cardoso , Alexandre Gramfort

We consider a setup in which confidential i.i.d. samples $X_1,\dotsc,X_n$ from an unknown finite-support distribution $\boldsymbol{p}$ are passed through $n$ copies of a discrete privatization channel (a.k.a. mechanism) producing outputs…

Machine Learning · Computer Science 2018-11-30 Adriano Pastore , Michael Gastpar

We consider theoretical limits of partial secrecy in a setting where an eavesdropper attempts to causally reconstruct an information sequence with low distortion based on an intercepted transmission and the past of the sequence. The…

Information Theory · Computer Science 2010-08-05 Paul Cuff

The generation of certifiable randomness is one of the most promising applications of quantum technologies. Furthermore, the intrinsic non-locality of quantum correlations allow us to certify randomness in a device-independent way, i.e. one…

Quantum Physics · Physics 2020-08-04 Brian Coyle , Elham Kashefi , Matty Hoban

In this paper, we present nontrivial upper and lower bounds on the secrecy capacity of the degraded Gaussian diamond-wiretap channel and identify several ranges of channel parameters where these bounds coincide with useful intuitions.…

Information Theory · Computer Science 2015-04-23 Si-Hyeon Lee , Ashish Khisti

Randomisation is a critical tool in designing distributed systems. The common coin primitive, enabling the system members to agree on an unpredictable random number, has proven to be particularly useful. We observe, however, that it is…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-05-25 Luciano Freitas , Petr Kuznetsov , Andrei Tonkikh

Since being proposed in 2006, differential privacy has become a standard method for quantifying certain risks in publishing or sharing analyses of sensitive data. At its heart, differential privacy measures risk in terms of the differences…

Information Theory · Computer Science 2025-11-19 Anand D. Sarwate , Flavio P. Calmon , Oliver Kosut , Lalitha Sankar

The Principle of Maximum Entropy is a rigorous technique for estimating an unknown distribution given partial information while simultaneously minimizing bias. However, an important requirement for applying the principle is that the…

Information Theory · Computer Science 2026-02-03 Kenneth Bogert , Matthew Kothe
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