Understanding the privacy relevant characteristics of data sets, such as reidentifiability and joinability, is crucial for data governance, yet can be difficult for large data sets. While computing the data characteristics by brute force is straightforward, the scale of systems and data collected by large organizations demands an efficient approach. We present KHyperLogLog (KHLL), an algorithm based on approximate counting techniques that can estimate the reidentifiability and joinability risks of very large databases using linear runtime and minimal memory. KHLL enables one to measure reidentifiability of data quantitatively, rather than based on expert judgement or manual reviews. Meanwhile, joinability analysis using KHLL helps ensure the separation of pseudonymous and identified data sets. We describe how organizations can use KHLL to improve protection of user privacy. The efficiency of KHLL allows one to schedule periodic analyses that detect any deviations from the expected risks over time as a regression test for privacy. We validate the performance and accuracy of KHLL through experiments using proprietary and publicly available data sets.

Does crowdsourcing work for web security? While the herculean task of evaluating hundreds of millions of websites can certainly benefit from the wisdom of crowds, skeptics question the coverage and reliability of inputs from ordinary users for assessing web security. We analyze the contribution patterns of serious and casual users in Web of Trust (WOT), a community-based system for website reputation and security. We find that the serious contributors are responsible for reporting and attending to a large percentage of bad sites, while a large fraction of attention on the goodness of sites come from the casual contributors. This complementarity enables WOT to provide warnings about malicious sites while differentiating the good sites from the unknowns. This in turn helps steer users away from the numerous bad sites created daily. We also find that serious contributors are more reliable in evaluating bad sites, but no better than casual contributors in evaluating good sites. We discuss design implications for WOT and for community-based systems more generally.

Third-party applications (apps) drive the attractiveness of web and mobile application platforms. Many of these platforms adopt a decentralized control strategy, relying on explicit user consent for granting permissions that the apps request. Users have to rely primarily on community ratings as the signals to identify the potentially harmful and inappropriate apps even though community ratings typically reflect opinions about perceived functionality or performance rather than about risks. With the arrival of HTML5 web apps, such user-consent permission systems will become more widespread. We study the effectiveness of user-consent permission systems through a large scale data collection of Facebook apps, Chrome extensions and Android apps. Our analysis confirms that the current forms of community ratings used in app markets today are not reliable indicators of privacy risks of an app. We find some evidence indicating attempts to mislead or entice users into granting permissions: free applications and applications with mature content request more permissions than is typical; 'look-alike' applications which have names similar to popular applications also request more permissions than is typical. We also find that across all three platforms popular applications request more permissions than average.