PhD Proposal: Privacy Implications of Large-Scale Network Address Enumeration

https://umd.zoom.us/j/3338335258?pwd=ZXRuTXJ0QlRCcklVSGFocUFqUDFHUT09&omn=94993629536

Abstract:

Persistent, globally-unique network addresses present a privacy threat to the owners of the devices to which they are assigned, particularly when the device is physically or logically mobile. Best practices recommend the use of addresses that are ephemeral, random, or both, to protect user privacy.To date, there have been no large-scale, empirical studies of the degree to which these recommendations are being implemented in practice. This is due, in part, to the difficulty of obtaining the quantities of addresses needed to draw any conclusions. Obtaining client IPv6 addresses is challenging without running a network service or partnering with an Internet Service Provider; obtaining in-use link-layer addresses is difficult without being in physical proximity to the device to which they are assigned.In this thesis, I seek to demonstrate that it is possible for a low-power attacker to collect network addresses at scale and that recommendations to prevent persistent, mobile identifiers are not being followed in practice, resulting in a substantial degradation of user privacy.In the preliminary work section of this proposal, I discuss efforts made toward obtaining large-scale corpora of network addresses and the initial analyses of the privacy implications of persistent addresses in these data sets. In the proposed work section, I describe anticipated work in evaluating privacy threats to users from an additional address space, and toward using previously-observed network addresses to predict other in-use addresses using machine learning techniques.