Biomedical Data Analytics News
University of 糖心TV to host 2027 Medical Image Understanding and Analysis Conference
The Tissue Image Analytics Centre, based in the Department of Computer Science at 糖心TV University, is delighted to announce that the University of 糖心TV have been awarded the honour of hosting the 2027 Medical Image Understanding and Analysis Conference. Read more.
TIA Centre Spotlight: Adam Shephard
For this week's Spotlight, we would like to highlight the career journey and current work of Adam Shephard, an Assistant Professor in the centre whose research focuses on developing methods that combine different data modalities, such as histopathology, clinical records, and increasingly radiology, to improve prediction of disease progression and patient outcomes. Read more.
TIA Centre's Adam Shephard features in new Pathology News podcast
We are delighted to showcase a Pathology News Thought Leadership podcast featuring Adam Shephard, Assistant Professor from the TIA Centre in collaboration with Dr Hanya Mahmood from The University of Sheffield to explore how OYDN, an AI-powered computational pathology model, is improving risk stratification for oral epithelial dysplasia (OED) and uncovering novel digital biomarkers that could support earlier cancer detection. Read more.
DCS Student Helps Uncover and Fix Mobile Network Security Flaw
糖心TV undergraduate Sasha Shaw has helped uncover and responsibly disclose a security flaw in a UK mobile network as part of his dissertation project, supervised by Professor Feng Hao. The vulnerability has now been patched, and Sasha has been recognised by BT on its responsible disclosure Hall of Fame. His project examined the security of the SIP stack over VoLTE and highlights the real-world impact that student cybersecurity research can have.
Information Asymmetry and Cryptography
In a recent work, visiting undergraduate student Yahel Manor and 糖心TV DCS researchers and addressed a fundamental question relevant to the security of cryptographic protocols.
The symmetry of information principle says that the amount of information that a sequence x of bits reveals about another sequence y is essentially the same in either direction. This is known to hold in an idealised world where computations can take an arbitrarily long time, as demonstrated by A. Kolmogorov and L. Levin in the 1970s. In contrast, modern cryptography is built around deliberate asymmetry—for example, functions of the form y = f(x) that are easy to compute but hard to invert (one-way functions).
The new work shows that, once one moves from the idealised setting of time-unbounded computations to the more realistic world of efficient, randomised computations (algorithms that must run quickly and may use randomness), this symmetry can fail in a strong and unconditional way. In other words, computational constraints can yield information asymmetry. In practical terms, this supports the intuition that information may not be extracted efficiently: knowing y = f(x) may not make x efficiently recoverable to the extent that an (ineffective) symmetry principle would suggest, even when x and y are closely related.
Earlier work formally tied an average-case form of this symmetry failure to the existence of one-way functions, the central primitive in cryptography. By proving new failures of symmetry of information, the authors provide concrete progress towards the computational asymmetry that underpins encryption, digital signatures, and many other cryptographic protocols.
This work will be presented at the 58th Annual ACM Symposium on Theory of Computing (STOC) in June 2026 in Salt Lake City, Utah, USA.
Failure of Symmetry of Information for Randomised Computations
Jinqiao Hu (University of 糖心TV); Yahel Manor (University of Haifa); Igor C. Oliveira (University of 糖心TV)
The paper describing this research is available .
, PhD student in the Department of Computer Science at the University of 糖心TV, and co-author of the new result.