Disco
Disco is a framework to implement machine learning algorithms that run in a browser. This allows testing new privacy-preserving decentralized ML algorithms.
Disco is a framework to implement machine learning algorithms that run in a browser. This allows testing new privacy-preserving decentralized ML algorithms.
The problem Here is a common scenario we have all run into: you need to communicate some piece of secret information, say a password, to another person. Perhaps it’s on-boarding a new colleague, or to allow access for a partner. But you don’t want to compromise this secret by transmitting it over an insecure channel, (…)
The Tandem / Monero project is a collaboration with Kudelski. It secures private keys in a privacy-preserving way.
Il n’aura pas fallu longtemps pour que les deep fakes, ces vidéos inventées de toutes pièces à partir de vidéos publiques par des algorithmes, s’invitent dans la guerre qui oppose la Russie à l’Ukraine depuis l’invasion de cette dernière le 24 février 2022. Le 16 mars dernier, des médias ukrainiens ont été piratés, diffusant une vidéo contrefaite du président ukrainien Volodymyr Zelensky annonçant la capitulation de l’armée ukrainienne. Peu après, c’est une fausse vidéo de Vladimir Poutine annonçant un accord de paix qui était diffusé par des hackers malveillants du camp adverse.
The track “AI & Healthcare” aims at bringing together researchers from academia, public health, start-ups and industry to share experiences and best practices, to jointly discuss the potential of AI in the transformation of healthcare towards a trustworthy system with improved patient and society outcomes.
By joining forces, C4DT and IMI aim to offer a track dedicated to societal issues related to information and technology, at the intersection between media and trust in the digital world.
In this track, we explore the role of AI for cybersecurity – its blessing and its curse – and how the private sector, government and academia should collaborate to reduce the threat landscape of AI systems as well as to isolate them with safeguard mechanisms that make it easy to shut down if things start to go wrong.
Basics The builder pattern is a well known coding pattern. It helps with object construction by having a dedicated structure to help build the other. It is usually used when many arguments are required to build one. The example codes are written in Rust, but the concepts behind these can be applied to many languages. (…)
Dès le lundi 7 mars, Heidi.news invite à prendre de la hauteur par rapport à la guerre en Ukraine et son flot incessant d’informations. Pour cette «semaine des spécialistes», nous sommes partis à la recherche d’esprits aiguisés pour nous aider à mieux comprendre ce qui se joue là, sous nos yeux, à notre porte. Ancienne analyste de l’armée américaine, Chelsea Manning était de passage à l’EPFL pour une conférence co-organisée par la Trust Valley sur le thème du futur des données et de la vie privée en temps de guerre. Heidi.news l’y a rencontrée.
Alors que toute l’Europe est en alerte face à une guerre informatique parallèle qui viendrait répondre aux représailles économiques déployées par les pays occidentaux contre la Russie, la lanceuse d’alerte Chelsea Manning relativise l’efficacité de ces attaques, dans le conflit en cours depuis le 24 février en Ukraine.
One of the ways public blockchains are touted is that they can replace your bank account. The idea is that you don’t need a central system anymore, but can open any number of accounts, as needed. However, as there is no central place, it is sometimes difficult to know how much money you have left. (…)
Today it’s something about actual hardware, not just software. For Christmas I took a long LED-strip and hooked it up to an Arduino One to create some animations. But not having WiFi was a bit of a shame, because this meant you couldn’t control it from a smartphone. So I dug around and found this: (…)
Privacy, security, and regulatory constraints create difficulties for data-driven projects. This includes initiatives involving sensitive data being processed, accessed, monetised, bought, sold, shared, aggregated, or analysed. To unleash the power of sensitive data for these functions, Privacy-Enhancing Technologies (PETs) are being deployed by many different sectors. Industries benefiting range from financial services to healthcare to pharmaceuticals to (…)
Confidential computing is an increasingly popular means to wider Cloud adoption. By offering confidential virtual machines and enclaves, Cloud service providers now host organizations, such as banks and hospitals, that abide by stringent legal requirement with regards to their client’s data confidentiality. Unfortunately, confidential computing solutions depend on bleeding-edge emerging hardware that (1) takes long to roll out at the Cloud scale and (2) as a recent technology, it is bound to frequent changes and potential security vulnerabilities. This proposal leverage existing commodity hardware combined with new programming language and formal method techniques and identify how to provide similar or even more elaborate confidentiality and integrity guarantees than the existing confidential hardware.
As machine learning (ML) models are becoming more complex, there has been a growing interest in making use of decentrally generated data (e.g., from smartphones) and in pooling data from many actors. At the same time, however, privacy concerns about organizations collecting data have risen. As an additional challenge, decentrally generated data is often highly heterogeneous, thus breaking assumptions needed by standard ML models. Here, we propose to “kill two birds with one stone” by developing Invariant Federated Learning, a framework for training ML models without directly collecting data, while not only being robust to, but even benefiting from, heterogeneous data.
Swisscom is joining the Nym network as a validator node. Nym is building the next generation of privacy infrastructure aiming to bring data privacy to all internet users. In doing so, Nym is leveraging blockchain technology to reward nodes that run the global privacy network.
In 2022, the #MITCDOIQ will bring the globally renowned Chief Data Officer & Information Quality Symposium (CDOIQ) hosted at MIT to Europe for the first time, in cooperation with the Competence Center Corporate Data Quality (University of Lausanne – UNIL) and the Swiss Data Science Center (ETH Zurich & EPFL). The CDOIQ Symposium has established itself as one of the most important data events to exchange innovative ideas, best practices and to promote the adoption of the CDO role.
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To promote research and education in cyber-defence, the EPFL and the Cyber-Defence (CYD) Campus have jointly launched the “CYD Fellowships – A Talent Program for Cyber-Defence Research.”
The fifth call for applications is now open with a rolling call for Master Thesis Fellowship applications, and with a deadline of 14 February 2022 (17:00 CEST) for Doctoral and Distinguished Postdoctoral Fellowship applications. Both new applications and resubmissions are strongly encouraged.
Some years ago, I was thinking that by directly look at code difference, I could estimate how faster it would run. I would reflect about complexity or how a given loop will be waay faster by precomputing some values. And of course, it is never that simple. Cache locality, threads synchronization and lock contention are (…)
We develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the current benchmark on simulated data. We use a long short- term memory (LSTM) neural network that is trained on labelled data generated by a process that experiences both jumps and volatility bursts. As a result, the network learns how to disentangle the two. Then it is applied to out-of-sample simulated data and delivers results that considerably differ from the benchmark: we obtain fewer spurious detection and identify a larger number of true jumps. When applied to real data, our approach for jump screening allows to extract a more precise signal about future volatility.
We’re currently using OmniLedger for logging in to our Matrix-chat and to the c4dt.org website as users. This is explained in more details here: CAS-login for OmniLedger Account management in OmniLedger C4DT partner login Matrix on Mobile There were two elements missing: Automatic signup — in the current signup process, the C4DT admin team needs (…)
This C4DT partner workshop will take place online and is by invitation only.
For all C4DT partners: Please send us an email if you are interested in participating. This workshop is limited to 20 participants. We still have few “screens” open.
Artificial Intelligence holds great promise for the economy and society. It has also led to the explosion of new ethical challenges, such as biased decision making, filter bubbles, data protection, face recognition, deep fakes and cyber security.
During this week we aim to create awareness of the ethical and privacy dilemmas with AI, explored from 4 different angles – business impact, cultural, technological and regulatory.
For more information, please click below