Educational Background

2018 – 2021 Doctor of Philosophy (PhD)

Swinburne University of Technology, Australia.

Thesis Title: Efficient Privacy Preservation in Smart Grid

2013 – 2017 Bachelor of Science in Electrical Engineering

COMSATS Institute of Information and Technology, Wah Cantt.

CGPA: 3.89/4.00 (Gold Medalist)

2011-2013 F.Sc/HSSC Pre-Engineering

Punjab College of Information Technology, BISE, Rawalpindi

Marks: 818/1100 (74.3%)

2009-2011 Matric/SSC Science

Usman Science School, BISE, Gujranwala

Marks: 838/1050 (79.8%)


  • Received IEEE TCSC Award for Excellence in Scalable Computing (ECR) for research excellence in privacy preservation of blockchain and decentralized energy systems.

  • Achieved “Gold Medal” from COMSATS Institute of Information Technology for being topper of Electrical Engineering Department.

  • “Academic Excellence Certificate and Award” in four Semesters for outstanding performance.

  • "Top Peer Reviewer 2019 Award" in the field of Computer Science, Engineering, and Cross-Field by by Publons (Clarivate Analytics - Web of Science Group).

  • "Top Reviewer Award" in Publons' global Peer Review Awards for being among top 1% reviewers (2017 - 2018). (PDF)

Outstanding Reviewer Award:

      • Elsevier Future Generation Computer Systems Journal, August 2018. (PDF)

      • Elsevier Journal of Network & Computer Applications, November 2018. (PDF)

      • Elsevier Computers and Electrical Engineering Journal, September, 2018. (PDF)

PhD Thesis

Thesis Title: Efficient Privacy Preservation in Smart Grid

  • Objectives:

Development of a secure and private smart grid system in which smart home users can benefit from modernized smart grid features without the risk of losing their privacy. Privacy preserving smart grid models have been designed for three most vulnerable scenarios of smart grid named as real-time smart metering, dynamic billing, and energy trading.

  • Challenges:

The first challenge is designing privacy preserving models via which smart homes users can contribute to efficient statistical analysis of smart grid in a private manner. The second challenge is to carry out dynamic billing of smart homes in an adversarial scenario where reporting billing values can lead to leakage of privacy. The third and final challenge is to design a decentralized, secure, and private energy trading scenario for smart grid prosumers alongside enhancing their social welfare.

  • Achievements:

      • A differential privacy based private smart metering model is proposed for efficient real-time usage reporting of smart home.

      • A privacy preserving demand response enhancing model is proposed to incentivize cooperative smart home users during dynamic billing alongside maintaining their privacy.

      • A thorough analysis for integration of differential privacy in blockchain technology has been carried out.

      • Analytical modelling for differentially private game-theoretic auctions for smart grid energy trading have been performed.

      • A differentially private VCG auction model is designed for blockchain based smart grid prosumers by considering the essence of privacy and social welfare enhancement.

      • Two energy-oriented miner selection models have been designed for blockchain based virtual power plants participating in smart grid energy trading.