Measuring the Carbon Footprint of Cryptographic Privacy-Enhancing Technologies

• URL: http://arxiv.org/abs/2508.04583v1
• Date: Wed, 06 Aug 2025 16:07:29 GMT
• Title: Measuring the Carbon Footprint of Cryptographic Privacy-Enhancing Technologies
• Authors: Marc Damie, Mihai Pop, Merijn Posthuma,
• Abstract summary: We measure the energy consumption and carbon footprint increase induced by five cryptographic PETs (compared to their non-private equivalent)<n>Our findings reveal significant variability in carbon footprint increases, ranging from a twofold increase in HTTPS web browsing to a 100,000-fold increase in encrypted ML.<n>Our study provides essential data to help decision-makers assess privacy-carbon trade-offs in such applications.
• Score: 0.7646713951724013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Privacy-enhancing technologies (PETs) have attracted significant attention in response to privacy regulations, driving the development of applications that prioritize user data protection. At the same time, the information and communication technology (ICT) sector faces growing pressure to reduce its environmental footprint, particularly its carbon emissions. While numerous studies have assessed the energy footprint of various ICT applications, the environmental footprint of cryptographic PETs remains largely unexplored. Our work addresses this gap by proposing a standardized methodology for evaluating the carbon footprint of PETs. To demonstrate this methodology, we focus on PETs supporting client-server applications as they are the simplest to deploy. In particular, we measure the energy consumption and carbon footprint increase induced by five cryptographic PETs (compared to their non-private equivalent): HTTPS web browsing, encrypted machine learning (ML) inference, encrypted ML training, encrypted databases, and encrypted emails. Our findings reveal significant variability in carbon footprint increases, ranging from a twofold increase in HTTPS web browsing to a 100,000-fold increase in encrypted ML. Our study provides essential data to help decision-makers assess privacy-carbon trade-offs in such applications. Finally, we outline key research directions for developing PETs that balance strong privacy protection with environmental sustainability.

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