Quantum computing is evolving fast and is challenging the security of the existing computing systems and the entire digital industry – beyond a certain threshold of computing power, it can break several classical public-key cryptography protocols. On the other end of the quantum evolution, quantum cryptography, random number generation, and quantum key distribution capabilities developed on top of quantum systems can support somewhat stronger security and privacy guarantees but are more expensive to support and can only be used with certain other constraints. Post-quantum cryptography, quantum random number generation, quantum key distribution, and quantum cryptography are evolving fast, and are building blocks of the remediation plan against quantum threats and for a viable quantum network in future. In this panel, we plan to discuss the post-quantum cryptography and security threats and what needs to be the technical and business execution plan to remain secure against such threats.

In the new digital world, data and its use governs the life of individuals. Data collection is ubiquitous and the use of Artificial Intelligence and Machine Learning tools often means that decisions are made regarding individuals using data that may or may not be collected with their consent and/or knowledge. This affects individuals in all spheres of their life: the private sphere, in public spaces, when receiving governmental services, and in their commercial activities. Fairness and equity is crucial to ensure that the results of advanced analytics provide equitable benefits to all people and do not unfairly benefit one subpopulation over another. At the same time, ensuring the privacy the data is necessary, given the sensitivity of personal data and the potential for its misuse. However, there may be an inherent tension between ensuring both privacy and fairness. This panel will explore several issues related to ensuring both privacy and fairness in analytics, including questions such as: Are privacy and fairness inherently conflicting? Is it at the data level or at the level of results? How do we balance fairness and privacy in the analysis of personal data? Can black-box algorithms be both privacy-preserving and fair? What are the key technical challenges in this area? What technological advances do you expect to see?

The SARS-CoV-2 virus was first identified to cause severe respiratory illness in December 2019. The World Health Organization (WHO) declared it as a Public Health Emergency of International Concern on 30 January 2020, and a pandemic on 11 March 2020. The whole world was quite unprepared for this pandemic of epic proportions that has caused severe public health, economic, and social disruptions, including the largest global recession since the Great Depression in the 1930s. Although, taken aback initially, the global scientific community has now taken the bull by its horns and began to fight back. Information Technology, especially developments in machine intelligence, collaborative computing and trust, security and privacy has played key role in the global war against the pandemic, starting with novel methodologies for rapid vaccine development, newer techniques for contact tracing, health monitoring, well-being and patient care, to efforts to fight spread of misinformation, enhance social support, remote workplace and so on. In this panel, four eminent scholars in the IT area discuss their views on what have been achieved so far and how we can be better prepared from an IT perspective to face Pandemic 2023 heads on.