Composability vs Inference Attacks: Ensuring Privacy in Data Release

In this research paper, we explore the possibility of reconstructing private data through a process called summation. We examine the conditions under which an adversary can successfully reconstruct private information from a collection of aggregated values, known as summaries. Our findings reveal that even with passive and honest-but-curious adversaries, it is possible to reconstruct private data with a relatively high success rate, especially in large networks.
To understand how this works, imagine you have a box of chocolates with your personal information inside. You give some of the chocolates to your friends, who then share them with other people in their social network. The adversary can then use these shared chocolates (summaries) to try and reconstruct your original information.
We found that the number of summations required for reconstruction depends on several factors, including the number of adversaries, the number of edges in the graph, and the number of neighbors each user has. In general, more adversaries and fewer neighbors lead to a higher success rate for reconstruction. However, we also found that the success rate is independent of the size of the full network.
Our results have important implications for privacy-preserving systems that rely on summarization. While these systems can provide strong anonymity guarantees in some cases, they may not be effective against determined adversaries who want to reconstruct private data. As such, it is crucial to develop new techniques and strategies to enhance the security of these systems and protect individual privacy.
In summary, our research demonstrates that summation-based privacy-preserving systems can be vulnerable to reconstruction attacks, especially when there are multiple adversaries involved. However, by understanding the factors that influence the success rate of these attacks, we can develop more effective countermeasures to safeguard individual privacy.