Nalin Ranasinghe

Ruwanga Konara, Kasun De Zoysa, Anuradha Mahasinghe et al.

With rapid advancements in quantum computing, it is widely anticipated that scalable quantum hardware may threaten classical cryptography and hence, the internet and the current information security infrastructure in the coming decade. This is mainly due to the operational realizations of quantum algorithms such as Grover and Shor, to which the current classical encryption protocols are vulnerable. Blockchains, i.e., blockchain data structures and their data, rely heavily on classical cryptography. One approach to secure blockchains is to attempt to achieve conceptual information-theoretic security under certain assumptions by defining blockchains on quantum technologies. There have been two major conceptualizations of blockchains data structures on quantum registers: the time-entangled Greenberger-Horne-Zeilinger (GHZ) state blockchain and the quantum hypergraph blockchain. We conceptualize a new quantum blockchain framework combining features of both these schemes to achieve the conceptual information-theoretic protection against undetected measurement attack (physics-based disturbance detectability) of the time-entangled GHZ blockchain and the scalability and efficiency of the quantum hypergraph blockchain in the proposed quantum blockchain data structure and framework. In this work, we propose a novel quantum blockchain architecture that integrates temporal GHZ entanglement with phase encoding inspired by the quantum hypergraph blockchain. The proposed design combines the conceptual information-theoretic tamper sensitivity/resistance of temporal entanglement with improved encoding efficiency, offering a unified conceptual framework for scalable and secure quantum blockchains.

Oshan Mudannayake, Nalin Ranasinghe

The amount of collected information on data repositories has vastly increased with the advent of the internet. It has become increasingly complex to deal with these massive data streams due to their sheer volume and the throughput of incoming data. Many of these data streams are mapped into graphs, which helps discover some of their properties. However, due to the difficulty in processing massive streaming graphs, they are summarized such that their properties can be approximately evaluated using the summaries. gSketch, TCM, and gMatrix are some of the major streaming graph summarization techniques. Our primary contribution is devising kMatrix, which is much more memory efficient than existing streaming graph summarization techniques. We achieved this by partitioning the allocated memory using a sample of the original graph stream. Through the experiments, we show that kMatrix can achieve a significantly less error for the queries using the same space as that of TCM and gMatrix.