A measure of personal information in mobile data

© 2020 IEEE. This paper describes fundamental aspects of a framework for privacy-preserving data sharing in a mobile context. The principal technical challenge is measuring the level of personal information (PI) in datasets that are shared for the delivery or enhancement of mobile enabled services. Another challenge is determining the threshold delineating a 'reasonable likelihood' of an individual being identifiable from the data. The risk of reidentification defines personally identifiable information (PII). The measure of PI must go beyond simply analysing personal attributes captured in data and consider preference revealed through use of services, temporal and spatial aspects of data, as well as context for use of services. Keywords-data sharing, privacy, mobile services

OptimShare: A Unified Framework for Privacy Preserving Data Sharing -- Towards the Practical Utility of Data with Privacy

Tabular data sharing serves as a common method for data exchange. However, sharing sensitive information without adequate privacy protection can compromise individual privacy. Thus, ensuring privacy-preserving data sharing is crucial. Differential privacy (DP) is regarded as the gold standard in data privacy. Despite this, current DP methods tend to generate privacy-preserving tabular datasets that often suffer from limited practical utility due to heavy perturbation and disregard for the tables' utility dynamics. Besides, there has not been much research on selective attribute release, particularly in the context of controlled partially perturbed data sharing. This has significant implications for scenarios such as cross-agency data sharing in real-world situations. We introduce OptimShare: a utility-focused, multi-criteria solution designed to perturb input datasets selectively optimized for specific real-world applications. OptimShare combines the principles of differential privacy, fuzzy logic, and probability theory to establish an integrated tool for privacy-preserving data sharing. Empirical assessments confirm that OptimShare successfully strikes a balance between better data utility and robust privacy, effectively serving various real-world problem scenarios