University of Sydney · IEEE Fellow · ACM Distinguished Scientist · Profile
Professor Athman Bouguettaya is a Professor of Computer Science at the University of Sydney, where he previously served as Head of School. He also held positions as Professor and Head of Computer Science and IT at RMIT University and was Science Leader in Service Computing at CSIRO (Data61). He is internationally recognized as a pioneer in Web Services and Service-Oriented Computing and has authored more than 330 publications in top-tier journals and conferences, including IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Internet Technology, ACM Computing Surveys, VLDB Journal, SIGMOD, ICDE, and EDBT. He serves as Editor-in-Chief of the Springer book series on Services Science and has held major leadership roles as General Chair of IEEE ICWS (2021-22), ICSOC (2020). Professor Bouguettaya is an ACM Distinguished Scientist, Member of Academia Europaea (Honoris Causa), and AAIA Fellow. He is listed among the Top 2% Scientists in Computer Science according to the Stanford University global study. His research has been supported by major funding agencies including ARC (Australia), NSF and NIH (USA), EU FP7, NSFC (China), and NPRP (Qatar).
A Service-based Approach to Drone Service Delivery in Skyway Networks
We propose a novel service framework to effectively provision drone-based delivery services in a skyway network. This service framework provides a high-level serviceoriented architecture and an abstraction to model the drone service from both functional and non-functional perspectives. We focus on spatio-temporal aspects as key parameters to query the drone services under a range of requirements, including drone capabilities, flight duration, and payloads. We propose to reformulate the problem of drone package delivery as finding an optimal composition of drone delivery services from a designated take-off station (e.g., a warehouse rooftop) to a landing station (e.g., a recipient’s landing pad). We select and compose those drone services that provide the best quality of delivery service in terms of payload, time, and cost under a range of intrinsic and extrinsic environmental (i.e., context-aware) factors, such as battery life, range, wind conditions, drone formation, etc. This talk will overview the key challenges and propose solutions in the context of single drones and swarms of drones for service delivery.
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University of Milan, Italy · IEEE Senior Member · ACM Distinguished Scientist · Profile
Dr. Ernesto Damiani is the Senior Director of Robotics and Intelligent Systems Institute at Khalifa University, UAE. He is also a Professor in the Electrical and Computer Engineering department and Director of the Khalifa University Center for Cyber Physical Systems (C2PS). He is on extended leave from the Department of Computer Science, Università degli Studi di Milano, Italy. His research interests include secure service-oriented architectures, privacy-preserving Big Data analytics and Cyber-Physical Systems security. With 494 publications listed on DBLP, he is considered among the most prolific European computer scientists. He is a senior member of the IEEE and ACM Distinguished Scientist.
Securing the Agentic Stream: From TESLA Hash Chains to Model-Independent Guardrails
Multi-agent systems powered by Large Language Models (LLMs) are transitioning from autonomous conversational demonstrators to distributed execution pipelines managing mission-critical operations. In these architectures, dynamic working memory—materialized as streaming token contexts and shared Key-Value (KV) cache structures—is passed across heterogeneous agents, retrieval pipelines, and external tools. However, modern inference engines treat working context as an implicit trust zone. This architectural blind spot exposes collaborative agent workflows to context poisoning, indirect prompt injections, state tampering in transit, and cache desynchronization. Conventional cryptographic defenses, such as asymmetric public-key signatures (PKI) over dynamic token streams or high-dimensional tensor pages, impose latency penalties that cripple real-time inference throughput. In this keynote, we present a unified framework bridging lightweight stream authentication with model-independent security architectures to establish Zero-Trust context provenance for multi-agent systems. We revisit the core mechanics of our Two-Level Initialization μTESLA (TLI-μTESLA) protocol, which decoupled stream bootstrapping from packet-rate message authentication through hierarchical one-way hash chains. We translate this paradigm into Context Hash Chains (CHCs) for agentic memory: high-level master chains bind workflow safety contracts and system invariants, while ephemeral low-level chains recursively anchor streaming token slices and agent handoffs at sub-microsecond overhead. Furthermore, we demonstrate how this lightweight cryptographic substrate interfaces with model-independent guardrails developed under systematic STRIDE threat modeling. By treating guardrails as out-of-band attestation services, the release of hash chain disclosure keys is dynamically gated upon automated security audits of intermediate context (such as raw retrieval payloads). Using a concrete, worked-out retriever–writer pipeline, we illustrate how context hash chains detect tampering in transit, neutralize prompt injection attacks, and enforce strict execution proofs prior to tool invocations, offering a reproducible, low-latency foundation for trustworthy multi-agent systems.
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