Towards Sustainable Cryptography: A Comprehensive Assessment of Compute Efficiency and Scope 1–3 Emissions for Partially Homomorphic Encryption in the Cloud

Quantum computing was in its infancy while cloud adoption increased but secure data processing methods in distributed environments became more important. As cloud-based operations continue to expand, allowing computation to be performed directly on encrypted data without the need for exposing private keys will be an important use case of homomorphic encryption. Fully homomorphic encryption (FHE) encompasses both addition and multiplication, whereas partial homomorphic encryption (PHE) is limited to either type of operation, which can provide practical efficiency benefits for certain applications. In this research, LightPHE, a Python-based PHE framework is implemented along with the implementation performance and environmental sustainability evaluation. The evaluation scope extends beyond the typical fair assessment of cryptography to include energy consumption profile and carbon emissions. The framework combines proven PHE algorithms and stays true to modular design principles as the foundation for secure application development. Experimental evaluations were performed on several cloud platforms such as Google Colab (Normal, A100 GPU, L4 GPU, T4 High RAM, TPU2) and Microsoft Azure Spark. The performance evaluation has included key generations, encryption, decryption and homomorphic operation, also the energy consumption was based on computational resource utilization. The environmental impact was evaluated via thorough analysis of Scope 1-3 emissions, as well as standardized data center efficiency metrics based on regional carbon intensity data. The study’s results showed unique trends in the trade-offs between computational performance and energy efficiency. Rather than calculating emissions for every algorithm variant, the analysis focuses on low and mid impact cases such as 80-bit and 128-bit resource-intensive homomorphic operations—where environmental considerations are most critical with Colab L4 GPU. Since the comparison with all cpu and gpu types including all data centers that will lead to another research. The results suggested that in both high-performance configurations and distributed environments, different optimization characteristics emerged in the energy-energy view where a lower total energy consumption is observed even at the expense of higher instantaneous demand. LightPHE offered uniform security across configurations, but differing environmental impact. This work empirically demonstrates the environmental considerations inherent to cryptographic implementations and lays the groundwork for future work in quantifiably assessing both security and sustainability in cloud-based encryption systems. The findings of this research provide practical recommendations for organizations looking to deploy secure, efficient, and sustainable data processing systems in modern cloud settings.