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S.R. Iyer
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Social science research produces a wealth of valuable data in the form of social networks. Making these empirical data public benefits research on socially relevant causes such as the spread of diseases, social interactions in a geographical location or online, movement and travel of people, flow of information in communication networks, etc. To protect the privacy of people represented in these networks while publicizing the data, anonymization is required. One way to identify an individual in network data is to study the local structure of the network around them. By altering the networks such that no node has a uniquely identifiable neighborhood, we can provide additional anonymity beyond concealing names and private identifiers.
The difficulty in this process of social network anonymization is that the network must remain useful to scientists, retaining properties important for downstream analysis. So far, research in this field has focused on maximizing anonymity and only measuring the utility of the anonymized data after the fact. The literature is also fragmented, with bespoke methods spanning different objectives unique to the dataset under consideration. This makes it costly for the data owner to understand and compare different approaches when their expertise lies in the domain of the data and not complex anonymization algorithms.
The main contribution of this thesis is a constraint programming-based framework in which scientists can easily explore the trade-offs between levels of anonymity for different attack models and the utility of the data for different measures. Within this sandbox, anonymity and utility requirements are expressed as independent, composable constraints. We model two structural anonymity measures of increasing strictness, k-degree and k-(degree, triangle), and introduce a betweenness-centrality preservation constraint that protects utility proactively by making modifications between central nodes expensive. We choose constraint programming for its declarative nature, which makes it easier to interpret the anonymization process. Evaluated on real-world networks, the framework shows that actively incorporating utility into the anonymization model is a promising direction. ...
The difficulty in this process of social network anonymization is that the network must remain useful to scientists, retaining properties important for downstream analysis. So far, research in this field has focused on maximizing anonymity and only measuring the utility of the anonymized data after the fact. The literature is also fragmented, with bespoke methods spanning different objectives unique to the dataset under consideration. This makes it costly for the data owner to understand and compare different approaches when their expertise lies in the domain of the data and not complex anonymization algorithms.
The main contribution of this thesis is a constraint programming-based framework in which scientists can easily explore the trade-offs between levels of anonymity for different attack models and the utility of the data for different measures. Within this sandbox, anonymity and utility requirements are expressed as independent, composable constraints. We model two structural anonymity measures of increasing strictness, k-degree and k-(degree, triangle), and introduce a betweenness-centrality preservation constraint that protects utility proactively by making modifications between central nodes expensive. We choose constraint programming for its declarative nature, which makes it easier to interpret the anonymization process. Evaluated on real-world networks, the framework shows that actively incorporating utility into the anonymization model is a promising direction. ...
Social science research produces a wealth of valuable data in the form of social networks. Making these empirical data public benefits research on socially relevant causes such as the spread of diseases, social interactions in a geographical location or online, movement and travel of people, flow of information in communication networks, etc. To protect the privacy of people represented in these networks while publicizing the data, anonymization is required. One way to identify an individual in network data is to study the local structure of the network around them. By altering the networks such that no node has a uniquely identifiable neighborhood, we can provide additional anonymity beyond concealing names and private identifiers.
The difficulty in this process of social network anonymization is that the network must remain useful to scientists, retaining properties important for downstream analysis. So far, research in this field has focused on maximizing anonymity and only measuring the utility of the anonymized data after the fact. The literature is also fragmented, with bespoke methods spanning different objectives unique to the dataset under consideration. This makes it costly for the data owner to understand and compare different approaches when their expertise lies in the domain of the data and not complex anonymization algorithms.
The main contribution of this thesis is a constraint programming-based framework in which scientists can easily explore the trade-offs between levels of anonymity for different attack models and the utility of the data for different measures. Within this sandbox, anonymity and utility requirements are expressed as independent, composable constraints. We model two structural anonymity measures of increasing strictness, k-degree and k-(degree, triangle), and introduce a betweenness-centrality preservation constraint that protects utility proactively by making modifications between central nodes expensive. We choose constraint programming for its declarative nature, which makes it easier to interpret the anonymization process. Evaluated on real-world networks, the framework shows that actively incorporating utility into the anonymization model is a promising direction.
The difficulty in this process of social network anonymization is that the network must remain useful to scientists, retaining properties important for downstream analysis. So far, research in this field has focused on maximizing anonymity and only measuring the utility of the anonymized data after the fact. The literature is also fragmented, with bespoke methods spanning different objectives unique to the dataset under consideration. This makes it costly for the data owner to understand and compare different approaches when their expertise lies in the domain of the data and not complex anonymization algorithms.
The main contribution of this thesis is a constraint programming-based framework in which scientists can easily explore the trade-offs between levels of anonymity for different attack models and the utility of the data for different measures. Within this sandbox, anonymity and utility requirements are expressed as independent, composable constraints. We model two structural anonymity measures of increasing strictness, k-degree and k-(degree, triangle), and introduce a betweenness-centrality preservation constraint that protects utility proactively by making modifications between central nodes expensive. We choose constraint programming for its declarative nature, which makes it easier to interpret the anonymization process. Evaluated on real-world networks, the framework shows that actively incorporating utility into the anonymization model is a promising direction.