Privacy in next generation networks

In the modern society, communications and digital transactions are becoming the norm rather than the exception. As we allow networked computing devices into our every-day actions, we build a digital lifestyle where networks and devices enrich our interactions. However, as we move our information towards a connected digital environment, privacy becomes extremely important as most of our personal information can be found in the network. This is especially relevant as we design and adopt next generation networks that provide ubiquitous access to services and content, increasing the impact and pervasiveness of existing networks. The environments that provide widespread connectivity and services usually rely on network protocols that have few privacy considerations, compromising user privacy. The presented work focuses on the network aspects of privacy, considering how network protocols threaten user privacy, especially on next generation networks scenarios. We target the identifiers that are present in each network protocol and support its designed function. By studying how the network identifiers can compromise user privacy, we explore how these threats can stem from the identifier itself and from relationships established between several protocol identifiers. Following the study focused on identifiers, we show that privacy in the network can be explored along two dimensions: a vertical dimension that establishes privacy relationships across several layers and protocols, reaching the user, and a horizontal dimension that highlights the threats exposed by individual protocols, usually confined to a single layer. With these concepts, we outline an integrated perspective on privacy in the network, embracing both vertical and horizontal interactions of privacy. This approach enables the discussion of several mechanisms to address privacy threats on individual layers, leading to architectural instantiations focused on user privacy. We also show how the different dimensions of privacy can provide insight into the relationships that exist in a layered network stack, providing a potential path towards designing and implementing future privacy-aware network architectures.