Collaborative and privacy-preserving estimation of IP address space utilisation

Exhaustion of the IPv4 address space is driving mitigation technologies, such as carrier-grade NAT or IPv6. Understanding this driver requires knowing how much allocated IPv4 space is actively used over time – a non-trivial goal due to privacy concerns and practical measurement challenges. To address this gap we present a collaborative and privacy-preserving capture-recapture (CR) technique for estimating IP address space utilisation. Public and private datasets of IP addresses observed by multiple independent collaborators can be combined for CR analysis, without any individual collaborator's privately observed addresses leaking to the others. We show that CR estimation is much more accurate than assuming all used addresses are observed, and that our scheme scales well to datasets of over a billion addresses across several collaborators. We estimate that 1.2 billion IPv4 addresses and 6.5 million /24 subnets were actively used at the end of 2014, and also analyse address usage depending on RIR and country

Private set intersection: A systematic literature review

Secure Multi-party Computation (SMPC) is a family of protocols which allow some parties to compute a function on their private inputs, obtaining the output at the end and nothing more. In this work, we focus on a particular SMPC problem named Private Set Intersection (PSI). The challenge in PSI is how two or more parties can compute the intersection of their private input sets, while the elements that are not in the intersection remain private. This problem has attracted the attention of many researchers because of its wide variety of applications, contributing to the proliferation of many different approaches. Despite that, current PSI protocols still require heavy cryptographic assumptions that may be unrealistic in some scenarios. In this paper, we perform a Systematic Literature Review of PSI solutions, with the objective of analyzing the main scenarios where PSI has been studied and giving the reader a general taxonomy of the problem together with a general understanding of the most common tools used to solve it. We also analyze the performance using different metrics, trying to determine if PSI is mature enough to be used in realistic scenarios, identifying the pros and cons of each protocol and the remaining open problems.This work has been partially supported by the projects: BIGPrivDATA (UMA20-FEDERJA-082) from the FEDER Andalucía 2014– 2020 Program and SecTwin 5.0 funded by the Ministry of Science and Innovation, Spain, and the European Union (Next Generation EU) (TED2021-129830B-I00). The first author has been funded by the Spanish Ministry of Education under the National F.P.U. Program (FPU19/01118). Funding for open access charge: Universidad de Málaga/CBU