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.
Prof. David Atienza, head of the C4DT affiliated Embedded Systems Laboratory (ESL) at EPFL School of Engineering and new Director of EcoCloud, has been elected as Chair of the European Design and Automation Association (EDAA). The EDAA pursues educational, scientific and technical activities for the advancement in the international community of electronic design and design automation.
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.
Olivier Crochat dirige le Center for Digital Trust, au sein de l’école polytechnique fédérale de Lausanne. Il revient sur le concept de confiance appliquée au monde digital avec un tour d’horizon des questions qui se posent aujourd’hui aux entreprises qui développent des services numériques basés sur la data et l’IA.
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.
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 (…)
I propose a new tool to characterize the resolution of uncertainty around FOMC press conferences. It relies on the construction of a measure capturing the level of discussion complexity between the Fed Chair and reporters during the Q&A sessions. I show that complex discussions are associated with higher equity returns and a drop in realized volatility. The method creates an attention score by quantifying how much the Chair needs to rely on reading internal documents to be able to answer a question. This is accomplished by building a novel dataset of video images of the press conferences and leveraging recent deep learning algorithms from computer vision. This alternative data provides new information on nonverbal communication that cannot be extracted from the widely analyzed FOMC transcripts. This paper can be seen as a proof of concept that certain videos contain valuable information for the study of financial markets.
We develop a methodology for detecting asset bubbles using a neural network. We rely on the theory of local martingales in continuous-time and use a deep network to estimate the diffusion coefficient of the price process more accurately than the current estimator, obtaining an improved detection of bubbles. We show the outperformance of our algorithm over the existing statistical method in a laboratory created with simulated data. We then apply the network classification to real data and build a zero net exposure trading strategy that exploits the risky arbitrage emanating from the presence of bubbles in the US equity market from 2006 to 2008. The profitability of the strategy provides an estimation of the economical magnitude of bubbles as well as support for the theoretical assumptions relied on.
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 (…)
We are delighted to announce that 3 additional start-ups have joined the C4DT community through the C4DT start-up program. For two years Duokey SA, Futurae Technologies AG and Nym Technologies SA will complement the already diverse group of partner companies through their start-up perspectives to collaborate and share insights on trust-building technologies. Their agility and innovation of has permitted these start-ups to differentiate themselves in their respective fields.
We are looking forward to an exciting and fruitful collaboration.
Please click below for more information.
By 2025 the total amount of data created, captured and consumed is predicted to reach 175 zettabytes. However, much of that data’s value is being wasted due to distrust, as there is fear that the data would be exposed when used, computed on or shared with collaborators, possibly leading to trade secrets being leaked or data protection legislation fines.
In this report we consider several real-life scenarios that may provoke causal research questions. As we introduce concepts in causal inference, we reference these case studies and other examples to clarify ideas and provide examples of how researchers are approaching topics using clear causal thinking.
The DEDIS team created a first version of the onChain secrets implementation using its skipchain blockchain. This implementation allows a client to store encrypted documents on a public but permissioned blockchain and to change the access rights to those documents after they have been written to the blockchain. The first implementation has been extensively tested by ByzGen and is ready to be used in a PoC demo.
This project aims at increasing its performance and stability, and make it production-ready. Further, it will add a more realistic testing platform that will allow to check the validity of new functionality in a real-world setting and find regressions before they are pushed to the stable repository.
On my path of moving lab’s code to more human friendly program, I usually write some CLIs, to ease configuration and deployment. When developing the client, I want to test it, and see how complex it is to use it. The best language to express that is a shell as it is probably how the (…)
To serve the 80 million forcibly-displaced people around the globe, direct cash assistance is gaining acceptance. ICRC’s beneficiaries often do not have, or do not want, the ATM cards or mobile wallets normally used to spend or withdraw cash digitally, because issuers would subject them to privacy-invasive identity verification and potential screening against sanctions and counterterrorism watchlists. On top of that, existing solutions increase the risk of data leaks or surveillance induced by the many third parties having access to the data generated in the transactions. The proposed research focuses on the identity, account, and wallet management challenges in the design of a humanitarian cryptocurrency or token intended to address the above problems. This project is funded by Science and Technology for Humanitarian Action Challenges (HAC).
Theoretical computer science and computer security
Modulation recognition state-of-the-art architectures use deep learning models. These models are vulnerable to adversarial perturbations, which are imperceptible additive noise crafted to induce misclassification, posing serious questions in terms of safety, security, or performance guarantees at large. One of the best ways to make the model robust is to use adversarial learning, in which the model is fine-tuned with these adversarial perturbations. However, this method has several drawbacks. It is computationally costly, has convergence instabilities and it does not protect against multiple types of corruptions at the same time. The objective of this project is to develop improved and effective adversarial training solutions that tackle these drawbacks.
![[FR] Chelsea Manning: «J’ai plus d’accès à la guerre en Ukraine avec mon laptop que je n’en avais en Irak»](https://c4dt.epfl.ch/wp-content/uploads/2022/03/B25DB3B9-DBA1-498F-B6E9-A3F04767C5D8.jpeg)
![[FR] «Dans cette guerre, la magie cyber s’épuise vite»](https://c4dt.epfl.ch/wp-content/uploads/2022/03/Screenshot-2022-04-30-at-11.30.59.png)
