VeriQloud

VeriQloud has been a member of the Quantum Internet Alliance (QIA) since day one. The vision of this project is based on the fact that each new development of quantum hardware would unlock new applications for quantum networks, without the need for full-scale quantum computers. Within the Alliance, VeriQloud has focused on collecting, assessing and simulating such applications, leading us to host the Quantum Protocol Zoo (wiki.veriqloud.com), an online encyplopedia of quantum communication protocols. Last week, two independent sources have confirmed the growing attention to these issues. In the US, the National Quantum Initiative Advisory Committee published a report on quantum networking. Among the recommendations: "an emphasis should be placed on identifying useful and impactful applications that require quantum networking and show promise to vastly improve performance over those without it." The field of quantum communication is also raising interests in the blockchain community, as can be seen in the Quantum Punks manifest, which states "We believe in using quantum physics to build uniquely new cryptography." The authors include among applications uncloneable encryption, device-independent protocols or one-shot signatures, going much beyond the current state-of-the-art of commercial quantum communication. These two documents published at the same time confirms our position. At VeriQloud, we've always pushed for more efforts on applications of quantum networks, believing that the field is largely underlooked. Over the years, we've managed to identify a few, but we also believe that a community effort between researchers, industries and startups is necessary for the field to grow. Today, we feel a certain pride and confidence to see a similar message coming from such different voices. Sources: * https://lnkd.in/eZmnmuQH * quantumpunks.org

🔒 Start Your Privacy-Preserving Journey with Shaffle! 🔒 Over the past weeks, we’ve shared our insights on Shaffle, VeriQloud’s cutting-edge solution for privacy-preserving federated learning. We’ve highlighted the privacy risks in standard federated learning and demonstrated how Shaffle mitigates these vulnerabilities to ensure data privacy for its users. Here’s a quick recap of the resources we’ve prepared to get you started: 📄 Article: Shaffle: A Framework for Secure Federated Learning https://lnkd.in/eYea93Vn 🎥 Video: Demo and Practical Implementation https://lnkd.in/edVccaef 📂 Jupyter Notebook: Run the Experiments Yourself! https://lnkd.in/gM2Rmdtp Ready to take the next steps? Do you have a model you’d like to train securely with federated learning? Contact us! We’ll make Shaffle available to you and work alongside you to protect the privacy of your users every step of the way. Let’s make privacy a standard, not a luxury. 🚀 #DataPrivacy #FederatedLearning #MachineLearning #Shaffle #CyberSecurity

Join us at the Montreal InCyber Forum, October 29-30, for a unique opportunity to engage in thought-provoking discussions on post-quantum cryptography. VeriQloud’s Head of Business & Research Development, North America, Didier Guignard, will share his insights during the roundtable panel: The Quantum Leap: Accelerating the Shift to Post-Quantum Cryptography for Global Security 🗓️ October 29, 10:40 AM - 11:40 AM Don’t miss the chance to hear Didier’s talk and meet him afterward for follow-up discussions. We look forward to connecting with you at the forum. --------------------------------------- Rejoignez-nous au Forum InCyber de Montréal les 29 et 30 octobre pour une occasion unique de participer à des discussions stimulantes sur la cryptographie post-quantique. Didier Guignard, Directeur du Développement Commercial et de la Recherche de VeriQloud en Amérique du Nord, partagera ses idées lors de la table ronde : Le Saut Quantique : Accélérer la Transition vers la Cryptographie Post-Quantique pour la Sécurité Globale 🗓️ 29 octobre, 10h40 - 11h40 Ne manquez pas l'opportunité d'écouter l'intervention de Didier et de le rencontrer après la session pour des discussions complémentaires. Nous avons hâte de vous retrouver lors du forum. #Quantum #Security #Privacy #Veriqloud #Quantumcommunication VeriQloud Cybersecurity in a Quantum World Discover our page on Linkedin, click on the bell and follow us 🌐 https://lnkd.in/egHFddwn

VeriQloud is excited to participate in the upcoming #PQC roundtable panel at the Montreal InCyber Forum. We look forward to engaging discussions during the two-day event on October 29-30. Here's a preview of the insightful topic Didier Guignard, VeriQloud Head of Business and Research Development in North America, will be addressing: 𝗧𝗵𝗲 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗟𝗲𝗮𝗽: 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝘁𝗼 𝗽𝗼𝘀𝘁-𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗿𝘆𝗽𝘁𝗼𝗴𝗿𝗮𝗽𝗵𝘆 𝗳𝗼𝗿 𝗴𝗹𝗼𝗯𝗮𝗹 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆. The 29 from 10H40 to 11H40 Don't miss this round table! so be certain to catch Didier Guignard after his talk with any follow-up questions. Looking forward to seeing you there. #veriqloud #cybersecurity #quantumsecurity #quantumcommunication FIC North America

🌟 Exciting Collaboration in Quantum Technology! 🌟 VeriQloud is thrilled to announce our collaboration with the University of Gdansk to address key challenges in quantum network technology deployment. This exciting partnership kicked off with a dedicated workshop at the ICTQT - International Centre for Theory of Quantum Technologies, where we delved into practical challenges and security measures for future quantum networks. Hosted by Dr. Marcin Pawłowski, leader of the Quantum Cybersecurity and Communication group, the event explored vulnerabilities and solutions for next-gen quantum communication technologies. ICTQT - International Centre for Theory of Quantum Technologies is renowned for its groundbreaking research, from quantum foundations to practical cybersecurity applications, under the visionary leadership of Prof. Marek Zukowski. His pioneering work in quantum secret sharing directly aligns with VeriQloud's innovations in secure key exchange and quantum cloud computing. Together, our aim is clear: to make quantum networks more secure, diverse in application, and truly beneficial for end-users. By combining ICTQT's theoretical expertise with VeriQloud’s real-world quantum platforms, we are pushing the boundaries of quantum cybersecurity and beyond. 🚀🔐 Looking forward to the amazing advancements ahead! 🌍✨ #QuantumSecurity #QuantumInternet #Cybersecurity #QuantumInnovation #VeriQloud #ICTQT

🚀 Making Secure Federated Learning Practical: Watch it in Action! 🚀 We’ve introduced the challenges of privacy in Federated Learning and explored how Shamir Secret Sharing can safeguard client data during aggregation. Now, it’s time to take it from theory to practice. In our new video, we walk you through the entire process: 1.Theory Recap: Building on our previous posts, we revisit the vulnerabilities in Federated Learning and how to counter them with Shamir Secret Sharing. 2.Jupyter Notebook Demo: We showcase a Jupyter Notebook where you can perform the same privacy attacks we discussed, using your own dataset to evaluate the risks. 3.Blind Federated Learning: See how to implement blind federated learning with Shaffle, using PyTorch for the ML tasks and Qasmat for secure aggregation. 4.Real-World Dataset: We apply this to the MNIST dataset, showing you how Shaffle works seamlessly with standard machine learning libraries. ->Download the Jupyter Notebook here: https://lnkd.in/gM2Rmdtp ->Learn more about Shaffle: https://lnkd.in/gtM_gTtk By combining PyTorch with Qasmat's secure infrastructure, you can fully protect your Federated Learning models without sacrificing compatibility or performance. This is your opportunity to take control of privacy in distributed learning! **Stay ahead in the game of data privacy** *******Watch our demo and start your journey with Shaffle today!***** https://lnkd.in/grysyhbh 🚀 Your data’s future starts here ! #DataPrivacy #FederatedLearning #Shaffle #MachineLearning #CyberSecurity

At VeriQloud, we take data privacy seriously #QuantumSecurity #Veriqloud #FederatedLearning #DataPrivacy

🔒 Strengthening Privacy in Federated Learning: A Comprehensive Approach! 🔒 Last week, we unveiled how a malicious server could compromise the privacy of Federated Learning by exploiting gradient information to recover original data. How do we prevent this? The key lies in having clients encrypt their gradients before sending them to the server. This encryption must not obstruct the server's ability to aggregate data. One particularly effective method for this is Shamir Secret Sharing. Shamir Secret Sharing is fundamental to VeriQloud's Qasmat software, which facilitates secure distributed storage. This innovative solution enables users to distribute data across multiple servers within a Qasmat cluster, ensuring that no single server has enough information to reconstruct the original data. In fact, each server individually holds no information about the dataset. Additionally, we can repurpose a Qasmat cluster for storage as an aggregation cluster. Instead of relying on a single server, clients send Shamir secret shares to multiple servers, allowing them to perform aggregation securely. Clients can then reconstruct the complete gradient from the shares received, enabling model updates while safeguarding privacy. As long as the servers remain non-communicative, they cannot extract any information about the client’s data. The workflow for this blind version of Federated Learning closely mirrors the standard approach, now requiring at least two servers. Clients will encrypt their data before transmission and decrypt it upon receipt, while standard machine learning operations continue unaffected. Stay tuned for next week! We will showcase how standard machine learning libraries can be integrated with Qasmat's cryptographic methods to implement blind Federated Learning effectively. #DataPrivacy #FederatedLearning #CyberSecurity #MachineLearning #Qasmat

Breaking the Privacy of Federated Learning: The Hidden Threats! At VeriQloud, we take data privacy seriously. Today, we kick off a special 3-part series focused on a critical vulnerability in Federated Learning. What is Federated Learning? It’s a method where devices work together to train a machine learning model—without sharing their raw data. Each device keeps its data local, only sending model updates (gradients) to a central server, which aggregates them into a global model. The Hidden Risk: While this sounds secure, here’s the problem: hackers who gain access to the server can reverse-engineer these gradients to reconstruct your original data! Our engineers ran a real-world test, using nothing but public algorithms and a simple laptop, and they were able to shatter privacy in minutes. Why Does This Matter? Consider the sensitive applications of Federated Learning—like medical data, energy consumption, or personal finance. Without strong privacy, these revolutionary use cases are simply too risky. What’s Next? Next week, we’ll reveal a powerful yet simple solution that makes Federated Learning truly privacy-proof—and it’s easier than you think! Stay tuned to learn how Qasmat ensures your data stays secure, no matter what. #DataPrivacy #FederatedLearning #CyberSecurity #MachineLearning #QuantumSecurity

Breaking the Privacy of Federated Learning: The Hidden Threats! At VeriQloud, we take data privacy seriously. Today, we kick off a special 3-part series focused on a critical vulnerability in Federated Learning. What is Federated Learning? It’s a method where devices work together to train a machine learning model—without sharing their raw data. Each device keeps its data local, only sending model updates (gradients) to a central server, which aggregates them into a global model. The Hidden Risk: While this sounds secure, here’s the problem: hackers who gain access to the server can reverse-engineer these gradients to reconstruct your original data! Our engineers ran a real-world test, using nothing but public algorithms and a simple laptop, and they were able to shatter privacy in minutes. Why Does This Matter? Consider the sensitive applications of Federated Learning—like medical data, energy consumption, or personal finance. Without strong privacy, these revolutionary use cases are simply too risky. What’s Next? Next week, we’ll reveal a powerful yet simple solution that makes Federated Learning truly privacy-proof—and it’s easier than you think! Stay tuned to learn how Qasmat ensures your data stays secure, no matter what. #DataPrivacy #FederatedLearning #CyberSecurity #MachineLearning #QuantumSecurity