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This large-scale measurement study of Twitter focuses on understanding how users control the longitudinal exposure of their publicly shared social data -- that is, their tweets -- and the limitations of currently used control mechanisms. The study finds that, while Twitter users widely employ longitudinal exposure control mechanisms, they face two fundamental problems. First, even when users delete their data or account, the current mechanisms leave significant traces of residual activity. Second, these mechanisms single out withdrawn tweets or accounts, attracting undesirable attention to them. To address both problems, an inactivity-based withdrawal scheme for improved longitudinal exposure control is explored.

Intentional or unintentional leakage of confidential data is undoubtedly one of the most severe security threats that organizations face in the digital era. The threat now extends to our personal lives: a plethora of personal information is available to social networks and smartphone providers and is indirectly transferred to untrustworthy third party and fourth party applications. In this work, we present a generic data lineage framework Lime for data flow across multiple entities that take two characteristic, principal roles (i.e., owner and consumer). We define the exact security guarantees required by such a data lineage mechanism toward identification of a guilty entity, and identify the simplifying non-repudiation and honesty assumptions. We then develop and analyze a novel accountable data transfer protocol between two entities within a malicious environment by building upon oblivious transfer, robust watermarking, and signature primitives. Finally, we perform an experimental evaluation to demonstrate the practicality of our protocol and apply our framework to the important data leakage scenarios of data outsourcing and social networks. In general, we consider Lime, our lineage framework for data transfer, to be an key step towards achieving accountability by design.