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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.

`derive_builder`: usage and limitations

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. (…)

[FR] Chelsea Manning: «J’ai plus d’accès à la guerre en Ukraine avec mon laptop que je n’en avais en Irak»

Posted on by Manon Michel

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.

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. (…)

Fun with microcontrollers

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-Enhancing Technology Summit Europe 2022

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 (…)

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.

Swisscom Joins Nym Privacy Blockchain

Posted on by Manon Michel

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.

The Inaugural Chief Data Officer & Information Quality European Symposium

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.

AI & Ethics test

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

Posted on by admin

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.

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 (…)

Deep Learning, Jumps, and Volatility Bursts

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.

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.

AI & Ethics Week

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

Demystifying the Commercial Potential of Privacy-Enhancing Technologies

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.

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.

Reusable CLI integration tests

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 (…)

PAIDIT: Private Anonymous Identity for Digital Transfers

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).