Privacy-preserving AI for Future Networks

ment mechanisms, Quality of Service (QoS) and Quality of Experience (QoE) management, network security and many other tasks.1 On the industrial side, AI is slowly complementing traditional networking approaches worldwide: Small Medium Enterprises (SMEs) and start-ups are developing AI solutions to deal with specific use cases, traditional networking vendors are evolving their products to support AI tools, and major cloud/software providers are adapting AI tools to be used in the networking domain. This is also the case for telephone service providers (telcos), which are exploring the application of AI algorithms through internal research and innovation (R&I) projects. For instance, our group is working on AI-based approaches in many use-cases focusing on realistic environments and applications (for example, Kattadige et al.2 and Perino property inference or data reconstruction attacks, and adversarial learning, can reveal different aspects of the data used (for example, which specific users’ data were used for model training), the values of their data attributes, and even user patterns such as their mobility or browsing behavior. Therefore, the use of AI techniques can impact user privacy more strongly than traditional data analysis methods since AI models can distill information from multiple data sources and infer rich patterns regarding et al.5), partially in collaboration with other European partners in the context of European R&I actions.b What about my privacy? AI models and tools are potentially vulnerable, and their usage introduces new attack vectors for telco environments. For instance, membership and