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[FR] Avis en ligne: souriez, vous êtes manipulé !

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C’est devenu un réflexe: avant de réserver un hôtel ou un restaurant sur Internet, on se précipite sur les avis des utilisateurs pour évaluer la qualité de l’établissement. Si les avis sont élogieux, on réserve dans la foulée. S’ils sont désastreux, on passe son chemin et on réservera le restaurant d’à côté….mais ces avis sont-ils crédibles ? S’agit-il seulement de vrais consommateurs ? Les plateformes de réservation contrôlent-elles la véracité des avis ? La réponse à ces questions est catégorique: absolument pas ! ABE a tenté une expérience spectaculaire qui prouve qu’on peut très facilement manipuler les avis des utilisateurs.

Disco

Disco is a framework to implement machine learning algorithms that run in a browser. This allows testing new privacy-preserving decentralized ML algorithms.

Magic-Wormhole: communicate a secret easily

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, (…)

Applied Machine Learning Days – AI & Healthcare

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.

Applied Machine Learning Days – AI & Cybersecurity

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.

How to read your bank-account on a public blockchain?

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. (…)

Tyche: Confidential Computing on Yesterday’s Hardware

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.

Invariant Federated Learning: Decentralized Training of Robust Privacy-Preserving Models

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.

[FR] Data et IA : comment les entreprises peuvent-elles générer plus de confiance pour leurs clients et utilisateurs ?

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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.

AI & Ethics test

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Cyber-Defence Fellowships – A Talent Program for Cyber-Defence Research in Switzerland

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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.

From Zürich to Dubai: Urban Digital Mobility

For the Travel & Connectivity Week at Expo 2020, the Swiss Pavilion is going to look at the mobility sector from the eyes of digitalisation. Matthias Finger, C4DT affiliated Professor, will be moderating the event.

hyperfine, benchmarks for CLIs

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 (…)

Risk & returns around FOMC press conferences: a novel perspective from computer vision

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.

Deep Learning for Asset Bubbles Detection

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.

OmniLedger email signup and recovery

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 (…)

AI & Ethics Workshop (by invitation only)

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.

Causal Inference Using Observational Data: A Review of Modern Methods

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.

Production-Readiness Timeline for Skipchains with onChain secrets

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.

Auditable Sharing and Management of Sensitive Data Across Jurisdictions

This work aims at creating a Proof of Concept of storing and managing data on a blockchain. This work answers the following two use-cases: (i) compliant storage, transfer and access management of (personal) sensitive data and (ii) compliant cross-border or cross-jurisdiction data sharing.

DEDIS brings to the table a permissioned blockchain and distributed ledger using a fast catch up mechanism that allows for very fast processing of the requests, while staying secure. It also includes a novel approach to encryption and decryption, where no central point of failure can let the documents be published to outsiders (Calypso). Swiss Re brings to the table interesting use cases which will require DEDIS to extend Calypso to implement data location policies.