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In contemporary cryptographic systems, secret keys are usually exchanged by means of methods, which suffer from mathematical and technology inherent drawbacks. That could lead to unnoticed complete compromise of cryptographic systems, without a chance of control by its legitimate owners. Therefore a need for innovative solutions exists when truly and reliably secure transmission of secrets is required for dealing with critical data and applications. Quantum Cryptography (QC), in particular Quantum Key Distribution (QKD) can answer that need. The business white paper (BWP) summarizes how secret key establishment and distribution problems can be solved by quantum cryptography. It deals with several considerations related to how the quantum cryptography innovation could contribute to provide business effectiveness. It addresses advantages and also limitations of quantum cryptography, proposes a scenario case study, and invokes standardization related issues. In addition, it answers most frequently asked questions about quantum cryptography.
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Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. However, solving federated machine learning problems raises issues above and beyond those of centralized machine learning. These issues include setting up communication infrastructure between parties, coordinating the learning process, integrating party results, understanding the characteristics of the training data sets of different participating parties, handling data heterogeneity, and operating with the absence of a verification data set. IBM Federated Learning provides infrastructure and coordination for federated learning. Data scientists can design and run federated learning jobs based on existing, centralized machine learning models and can provide high-level instructions on how to run the federation. The framework applies to both Deep Neural Networks as well as ``traditional approaches for the most common machine learning libraries. {proj} enables data scientists to expand their scope from centralized to federated machine learning, minimizing the learning curve at the outset while also providing the flexibility to deploy to different compute environments and design custom fusion algorithms.
The NSF has chosen the site for the Deep Underground Science and Engineering Laboratory (DUSEL) to be in Lead, South Dakota. In fact, the state of South Dakota has already stepped up to the plate and contributed its own funding for the proposed lab, see http://www.sanfordlaboratoryathomestake.org/index.html. The final decision by NSF for funding the Initial Suite of Experiments for DUSEL will be made early in 2009. At that time the NSF Science Board must make a decision. Of order 200 experimentalists have already expressed an interest in performing experiments at DUSEL. In order to assess the interest of the theoretical community, the Center for Cosmology and Astro-Particle Physics (CCAPP) at The Ohio State University (OSU) organized a 3-day DUSEL Theory Workshop in Columbus, Ohio from April 4 - 6, 2008. The workshop focused on the scientific case for six proposed experiments for DUSEL: long baseline neutrino oscillations, proton decay, dark matter, astrophysical neutrinos, neutrinoless double beta decay and N-Nbar oscillations. The outcome of this workshop is the DUSEL Theory White paper addressing the scientific case at a level which may be useful in the decision making process for policy makers at the NSF and in the U.S. Congress. In order to assess the physics interest in the DUSEL project we have posted the DUSEL Theory White paper on the following CCAPP link http://ccapp.osu.edu/whitepaper.html . Please read the white paper and, if you are interested, use the link to show your support by co-signing the white paper.
The ILC Higgs White Paper is a review of Higgs Boson theory and experiment at the International Linear Collider (ILC). Theory topics include the Standard Model Higgs, the two-Higgs doublet model, alternative approaches to electroweak symmetry breaking, and precision goals for Higgs boson experiments. Experimental topics include the measurement of the Higgs cross section times branching ratio for various Higgs decay modes at ILC center of mass energies of 250, 500, and 1000 GeV, and the extraction of Higgs couplings and the total Higgs width from these measurements. Luminosity scenarios based on the ILC TDR machine design are used throughout. The gamma-gamma collider option at the ILC is also discussed.
The SARS-CoV-2 (Covid-19) pandemic has caused significant strain on public health institutions around the world. Contact tracing is an essential tool to change the course of the Covid-19 pandemic. Manual contact tracing of Covid-19 cases has significant challenges that limit the ability of public health authorities to minimize community infections. Personalized peer-to-peer contact tracing through the use of mobile apps has the potential to shift the paradigm. Some countries have deployed centralized tracking systems, but more privacy-protecting decentralized systems offer much of the same benefit without concentrating data in the hands of a state authority or for-profit corporations. Machine learning methods can circumvent some of the limitations of standard digital tracing by incorporating many clues and their uncertainty into a more graded and precise estimation of infection risk. The estimated risk can provide early risk awareness, personalized recommendations and relevant information to the user. Finally, non-identifying risk data can inform epidemiological models trained jointly with the machine learning predictor. These models can provide statistical evidence for the importance of factors involved in disease transmission. They can also be used to monitor, evaluate and optimize health policy and (de)confinement scenarios according to medical and economic productivity indicators. However, such a strategy based on mobile apps and machine learning should proactively mitigate potential ethical and privacy risks, which could have substantial impacts on society (not only impacts on health but also impacts such as stigmatization and abuse of personal data). Here, we present an overview of the rationale, design, ethical considerations and privacy strategy of `COVI, a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
ZeroDB is an end-to-end encrypted database that enables clients to operate on (search, sort, query, and share) encrypted data without exposing encryption keys or cleartext data to the database server. The familiar client-server architecture is unchanged, but query logic and encryption keys are pushed client-side. Since the server has no insight into the nature of the data, the risk of data being exposed via a server-side data breach is eliminated. Even if the server is successfully infiltrated, adversaries would not have access to the cleartext data and cannot derive anything useful out of disk or RAM snapshots. ZeroDB provides end-to-end encryption while maintaining much of the functionality expected of a modern database, such as full-text search, sort, and range queries. Additionally, ZeroDB uses proxy re-encryption and/or delta key technology to enable secure, granular sharing of encrypted data without exposing keys to the server and without sharing the same encryption key between users of the database.
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