April 2020


  • Jagriti Jalal, Mayank Singh, Arindam Pal, Lipika Dey and Animesh Mukherjee, ‘Identification, Tracking and Impact: Understanding the trade secret of catchphrases’, ACM/IEEE Joint Conference on Digital Libraries (JCDL 2020), CORE A* conference.
  • Rizka Purwanto, Arindam Pal, Alan Blair, Sanjay Jha, ‘PhishZip: A New Compression-based Algorithm for Detecting Phishing Websites’, IEEE Conference on Communications and Network Security (CNS 2020).
  • Huy Quoc Le, Dung Hoang Duong, Willy Susilo and Josef Pieprzyk, ‘Trapdoor Delegation and HIBE from Middle-Product LWE in Standard Model’, ACNS 2020 : 18th International Conference on Applied Cryptography and Network Security.
  • Viet Vo, Shangqi Lai, Xingliang Yuan, Shi-Feng Sun, Surya Nepal and Joseph K. Liu, ‘Accelerating Forward and Backward Private Searchable Encryption Using Trusted Execution’, ACNS 2020 : 18th International Conference on Applied Cryptography and Network Security, accepted March 24, 2020.
  • Cong Zuo, Shifeng Sun, Joseph Liu, Jun Shao, Josef Pieprzyk, Lei Xu, ‘Forward and Backward Private DSSE for Range Queries’, IEEE Transactions on Dependable and Secure Computing, (accepted March 2020).
  • Raphael Phan, Masayuki Abe, Lynn Batten, Jung Cheon, Ed Dawson, Steven Galbraith, Jian Guo, Lucas Hui, Kwangjo Kim, Xuejia Lai, Dong Hoon Lee, Mitsuru Matsui, Tsutomu Matsumoto, Shiho Moriai, Phong Nguyen, Dingyi Pei, Duong Hieu Phan, Josef Pieprzyk, Huaxiong Wang, Hank Wolfe, Duncan Wong, Tzong-Chen Wu, Bo-Yin Yang, Siu-Ming Yiu, Yu Yu, Jianying Zhou, ‘Advances in Security Research in the Asiacrypt Region’, Communications of the ACM, April 2020, Vol 63, No 4, pp 1-6.
  • Yang Liu, Zhuo Ma, Ximeng Liu, Siqi Ma, Surya Nepal, Robert H. Deng, and Kui Ren, ‘Boosting Privately: Communication-Optimized Federated Boosting for Mobile Crowdsensing’, To appear in the proceedings of the 40th International Conference on Distributed Computing System (ICDCS).
  • Derek Weber, Mehwish Nasim, Lucia Falzone, Lewis Mitchell, ‘#ArsonEmergency and Australia’s “Black Summer”: Polarisation and misinformation on social media’, accepted at MISDOOM symposium, https://2020.misdoom.org
  • Shuo Wang, Chen Tianle, Chen Shanyu, Rudolph Carsten, Nepal Surya and Grobler Marthie, ‘OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning’, International Joint Conference on Neural Networks (IJCNN).
  • B. Palaniswamy, Seyit Camtepe, E. Foo and Josef Pieprzyk, ‘An Efficient Authentication Scheme for Intra-vehicular Controller Area Network’, in IEEE Transactions on Information Forensics and Security (IEEE TIFS), 10.1109/TIFS.2020.2983285 (early access). Core-A Journal.


  • DSS have joined forces with the Cyber Security Cooperative Research Centre (CSCRC) and UNSW Canberra to investigate the cyber security risks and mitigations of organisations in response to the COVID-19 pandemic, considering the recent and significant increase in the number of people working from home.

To understand the impact of significant numbers of the workforce now working remotely and take-up of the work from home cyber security guidance, we are engaging with a number of businesses to understand and evaluate their controls and mitigating actions to address the additional exposure to cyber security related risk in the context of COVID-19.Our research will be designed to inform policy and guidance around cyber security for future outbreaks and play a role in how Australian businesses can factor this into cyber resilience planning in order to better protect Australian businesses.

  • Our DST/Data61/Swinburne CRP “Autonomic Computing for Resilient Cyber Operations in Contested Environments” has been granted a 12-month continuation (with associated funding), until June 2021.
  • The Melbourne team is working with two student teams from Swinburne University on the development of the Executive Cyber Training Platform and the Collaborative Group Decision-Making Platform as part of its University engagement.
  • We live through unprecedented times. Globally, the coordinated response to the COVID-19 pandemic has led to a large volume of from-home workers that use IT to communicate with colleagues and undertake their work from home.

With many companies implementing national guidance and protecting their workforce during the COVID-19 outbreak, the increased use of Bring Your Own Device (BYOD) and connectivity through shared home-environments is presenting additional risks to the cyber security of these organisations.For more information on what organisations need to consider when preparing for the majority of their workforce to work from home, and the 3 fundamental principles of cybersecurity – user, usage and usability, read the interview of our group leader, Dr Surya Nepal in the Algorithm.https://algorithm.data61.csiro.au/cybersecurity-considerations-amidst-a-global-pandemic/?utm_source=Algorithm24_CybersecurityWFH&utm_medium=article_link&utm_campaign=CybersecurityWFH_Algo24&utm_term=CybersecurityWFH_Algo24&utm_content=CybersecurityWFH_Algo24

  • An algorithm that detects social bot activity on Twitter in real-time could prevent the spread of misinformation and make it easier for first responders to detect major events according to a Research Scientist at CSIRO’s Data61.

The algorithm uses machine learning, artificial intelligence (AI) and natural language processing (NLP) to distinguish between genuine conversations and bot-generated messages, creating a set of ‘factual’ parameters and rapid filtering system for real-time results.Developed by Data61’s Dr Mehwish Nasim in collaboration with Dr Jonathan Tuke, Dr Lewis Mitchell, Prof Nigel Bean and Andrew Nguyen from University of Adelaide, the original concept was to solely detect major events, however, after finding much of the research data polluted by automated posts, the need to remove these users arose.For more information https://algorithm.data61.csiro.au/how-an-algorithm-is-could-prevent-the-spread-of-misinformation-and-improve-emergency-response-times/?utm_source=Algorithm_misinformation_filter_article&utm_medium=social_media&utm_campaign=Algorithm_misinformation_filter_article&utm_term=social_media_filtering_algorithm&utm_content=Algorithm_misinformation_filter_articleStudents

  • We are welcoming the first students novating to Data61 using the Data to Decisions CRC PhD legacy Fund. Welcome Maisie Badami, Alex Long, Dennis Liu, Caitlin Gray, and Samudra Herath.

Samudra is currently a PhD student in mathematics and computer science at the University of Adelaide. She is interested in developing entity resolution (ER) methods to handle big data. Accurate and efficient ER has been a problem in data analysis and data mining projects for decades. ER is an important, required step in data integration when identifying a group of entities (records) representing the same real-world entity in multiple databases. With the advent of big data computations, the demand for scalable ER techniques has increased and she is looking at new challenges big data brings in to the context of ER.

The most exciting thing in doing research is to have the flexibility of pursuing new ideas at the same time as contributing to knowledge. As my advisors always say PhD is a rollercoaster. I have good days and bad days but the journey goes on and I am passionate.’

To get to know them better, have a look at their presentation video.

Shangqi Lai submitted his thesis in March, he was one of the first students in our group. Well done Shangqi.

‘I’m Shangqi Lai, a PhD student at Monash University. My thesis title is “Privacy-Preserving Social Search: Primitives and Realisation”. In the past three years, I worked under the supervision of Dr. Dongxi Liu.My research aims to investigate how to support efficient and versatile social search functionality while protecting data confidentiality. In this research project, we consider designing and building a social search framework that supports the search functionality on the encrypted social graph with a strong privacy guarantee. As a result, we present a practical graph database system for privacy-preserving social search (GraphSE2). The proposed system adopts a distributed graph model and customised cryptographic primitives to support a group of atomic operations efficiently. Those operations can be composed to support more complicated real-world query operators over the encrypted data. We further build a real-world application to show that the system supports a wide range of social search applications over a large-scale encrypted social graph with low overheads. In addition, we review the security of the underlying cryptographic primitives in the system. We realised that the OXT protocol, which is adapted to support boolean queries in GraphSE2, has an existing attack based on its leakage. To address this attack, this thesis presents a new cryptographic scheme (HXT) for the proposed framework that eliminates the vulnerable leakage and supports the conjunctive query. Furthermore, HXT retains the efficiency when processing queries; deploying the HXT protocol only introduces a moderate overhead comparing to OXT.It has been a real pleasure to conduct my research at Data61. I would not have been able to complete my project and have publications in top-tier cybersecurity venues without accessing the awesome scientific computing infrastructures provided by Data61. Besides, Data61 hosted many interesting events, such as the cybersecurity summer school, which enabled me to meet and exchange ideas with other researchers and experts and allowed me to learn a lot from them.’