Graphs have become increasingly popular for modeling data in a wide variety of applications, and graph summarization is a useful technique to analyze information from large graphs. Privacy preserving mechanisms are vital to protect the privacy of individuals or institutions when releasing aggregate numbers, such as those in graph summarization. We propose privacy-aware release of graph summarization using zero-knowledge privacy (ZKP), a recently proposed privacy framework that is more effective than differential privacy (DP) for graph and social network databases. We first define group-based graph summaries. Next, we present techniques to compute the parameters required to design ZKP methods for each type of aggregate data. Then, we present an approach to achieve ZKP for probabilistic graphs.

Privacy preservation is important for machine learning and data mining, but measures designed to protect private information often result in a trade-off: reduced utility of the training samples. This paper introduces a privacy preserving approach that can be applied to decision tree learning, without concomitant loss of accuracy. It describes an approach to the preservation of the privacy of collected data samples in cases where information from the sample database has been partially lost. This approach converts the original sample data sets into a group of unreal data sets, from which the original samples cannot be reconstructed without the entire group of unreal data sets. Meanwhile, an accurate decision tree can be built directly from those unreal data sets. This novel approach can be applied directly to the data storage as soon as the first sample is collected. The approach is compatible with other privacy preserving approaches, such as cryptography, for extra protection.