Abstract: Over the last 16 years, blockchain has promised to solve many problems but has also encountered many hurdles. In this talk, we will talk about the history of blockchains and how it evolved over the years to become an intellectually challenging subject of research for various disciplines. We will explore existing challenges and potential mitigating solutions to describe what blockchain could look like tomorrow.

Abstract: In the cyber world, anonymous authentication is an important tool for privacy protection. However, users may misbehave under the cover of anonymity. Thus, accountability is crucial in any practical privacy-preserving authentication. Balancing anonymity and accountability has always been a challenging research problem. Accountable Anonymous credentials are the cryptographic schemes designed to address this challenge. In this talk, I will recall the concept of anonymous credentials and discuss various accountability mechanisms. Also, I will talk about its real-world applications and the challenges faced when constructing this cryptographic primitives.

Abstract: Attribute-based encryption (ABE) is an important technology in building access control systems with precise control and scalability. In an ABE system, there exists a private key generator (PKG) that issues all private keys. The PKG has a significant drawback referred to as the huge key management burden in large-scale user systems. To overcome this limitation, we propose a more flexible system that offers users the choice to utilize decryption keys either from the PKG or from trusted users to decrypt the ciphertext, reducing the workload of the PKG. Unfortunately, users are restricted to only receiving private keys from the PKG in most ABE schemes. Thus, our system ABE extends the ability of trusted users to generate and distribute decryption keys. Furthermore, decryption keys from trusted users possess equivalent decryption with a private key from the PKG when satisfying the cooperative access policy set by the encryptor. We present a key cooperative ABE scheme, defining the concept of key cooperative ABE for the first time.

Abstract: Withdrawable signature, a recently introduced variant of digital signature, addresses the need for flexibility in existing digital signatures by allowing signers to retract their signatures securely and efficiently. To achieve this, a withdrawable signature scheme initially creates an “unverifiable” signature on the signer’s public key, which can later be converted into a conventional, verifiable signature only by the signer. Previously, there were only two specific constructions using Schnorr and pairing. Recognizing the practical importance of the RSA signature, we aim to provide a generic construct of the withdrawable signature from the hash-then-one-way type signature, with RSA being a concrete instantiation.

We revisit and extend the definition and security notions of the existing withdrawable signature, introducing the concept of the “extended withdrawable signature” that extends the verification of the withdrawable signature from certain verifiers only to allow universal verification — a feature not achieved by previous work. We provide formal security analysis to demonstrate that our generic construction satisfies the revisited security notions of the withdrawable signature. This approach broadens the applicability and enhances the security of withdrawable signatures in various cryptographic applications.

Abstract: I will be talking about my various contributions to the secure and efficient implementation techniques and optimisations of lattice-based cryptographic primitives and protocols across multiple programming languages, CPUs, GPUs, and constrained IoT devices and Networks.

Panellists:

• A/Professor Claudia Szabo, The University of Adelaide
• Dr Cheryl Pope, The University of Adelaide
• Professor Abelardo Pardo, The University of Adelaide

Panellists:

• Professor Ben Rubenstein, The University of Melbourne
• Professor Anton van den Hengel, The University of Adelaide
• Kylie Walker, Australian Academy of Technological Sciences and Engineering
• Ann Nicholson, Monash University

Facilitator:

Professor Shazia Sadiq FTSE is a professor of computer science at the University of Queensland. As a researcher and educator in data and process management, her work has focused on dismantling socio-technical barriers to technology-driven transformation. As director of the ARC Industry Transformation Training Centre for Information Resilience, she has helped link research and industry through industry-informed PhD training programs. Shazia actively engages in policy advice and science advocacy activities, including the development of national strategic plans and expert submissions to government initiatives on emerging digital technologies, and was a core author on the government’s Rapid Response Information Report (2023) on Generative AI. She is also a champion for equity and diversity through her work with the first Australian ACM-W student chapter and DEI@DB, an international group that leads diversity, equity, and inclusion efforts for the database community. Shazia is the past Chair of the National Committee on Information and Communication Sciences at the Australian Academy of Science 2019-2022, is a fellow of the Australian Academy of Technological Sciences and Engineering, and member of The Australian Research Council College of Experts 2018-2021.

Empirical research in software engineering, as in the natural sciences, involves building models of the problem domain — in our case, software and its development — and then evaluating those models against real-world evidence. There is often pressure on researchers to “think big” to discover actionable truths that pertain broadly to software development. In this talk, I discuss the value of doing empirical “deep dives” into the study of individual software systems to better understand their social and technical contexts. Using example studies to illustrate, I argue that time spent better understanding individual systems can lead to deeper insights about the problem space and improve awareness of the holes, ambiguities, and naive mistakes in our models.

Biography

Michael W. Godfrey is a Professor in the David R. Cheriton School of Computer Science at the University of Waterloo (UW), which he joined in 1998. He is co-founder of the Software Analytics Group (SWAG), and is a Senior Member of both the ACM and IEEE. He has held an NSERC Industrial Research Associate Chair in telecommunications software engineering (2000-2005), and a UW David R. Cheriton Faculty Fellowship (2014-17). He has won three “best paper” awards, and one “most influential paper award” at various conferences. His research interests span many areas of empirical software engineering including software evolution, code review, reverse engineering, program comprehension, mining software repositories, and software clone detection and analysis. He has also contributed chapters to several books, including “Copy-Paste as a Principled Engineering Tool” (“Making Software: What Really Works and Why We Believe It” O’Reilly, 2010) “Why Provenance Matters” (“Perspectives on Data Science for Software Engineering”, Morgan-Kaufmann, 2016), and “Sometimes, cloning is a sound design decision!”, (“Code Clone Analysis: Researches, Tools, and Practices, Springer, 2021).

Airway segmentation is a prerequisite for diagnosing and screening pulmonary diseases. While computer aided algorithms have achieved great success in various medical image segmentation tasks, it remains a challenge in keeping the continuity of airway branches due to the special tubular shape. Some existing airway-specific segmentation models introduce topological representations such as neighbor connectivity and centerline overlapping into deep models and some other methods proposed customized network modules or training strategies based on the characteristics of airways. In this paper, we propose a large-kernel attention block to enlarge the receptive field as well as maintain the details of thin branches. We reformulate the segmentation problem into pixel-wise segmentation and connectivity prediction with a differentiable connectivity modeling technique, and also propose a self-correction loss to minimize the difference between these two tasks. In addition, the binary ground truth is transformed into distances from the boundary, and distance regression is used as additional supervision. Our proposed model has been evaluated on two public datasets, and the results show that our model outperforms other benchmark methods.

Diffusion-weighted MRI (dMRI) is an invaluable MRI technique in neuroimaging analysis, since it enables non-invasive probing of brain microstructures. A diffusion tensor imaging (DTI) model can be fitted on diffusion-weighted imaging (DWI) to characterize brain tissues by extracting several diffusion measure metrics such as Fractional Anisotropy (FA), Mean Diffusivity (MD), Axial Diffusivity (AD), and Radial Diffusivity (RD). These metrics are widely used in clinical studies for observing the group differences between health controls and patients. However, clinical acquisition constraints often lead to low angular resolution diffusion imaging (LARDI) and diffusion measure metrics derived from LARDI are unreliable. To obtain trustworthy diffusion measure metrics from LARDI for clinical studies, we propose High Angular Resolution Diffusion Tensor Imaging Estimation Network (HADTI-Net) to generate the enhanced low angular resolution DTI (LAR-DTI) from a minimal set of evenly distributed diffusion-weighted directions. We trained and evaluated HADTI-Net on the Human Connectome Project (HCP) dataset. The results show that the enhanced LAR-DTI by HADTI-Net was able to derive diffusion measure metrics comparable to those derived from high angular resolution DTI (HAR-DTI). Further extensive experiments demonstrate HADTI-Net’s clinical impact in reducing diffusion measure metric differences, enabling the observation of group distinctions between healthy controls and patients with neurological diseases.