Biblio

mHealth apps have a vital role in facilitation of human health management. Users have to enter sensitive health related information in these apps to fully utilize their functionality. Unauthorized sharing of sensitive health information is undesirable by the users. mHealth apps also collect data other than that required for their functionality like surfing behavior of a user or hardware details of devices used. mHealth software and their developers also share such data with third parties for reasons other than medical support provision to the user, like advertisements of medicine and health insurance plans. Existence of a comprehensive and easy to understand data privacy policy, on user data acquisition, sharing and management is a salient requirement of modern user privacy protection demands. Readability is one parameter by which ease of understanding of privacy policy is determined. In this research, privacy policies of 27 free Android, medical apps are analyzed. Apps having user rating of 4.0 and downloads of 1 Million or more are included in data set of this research.RGL, Flesch-Kincaid Reading Grade Level, SMOG, Gunning Fox, Word Count, and Flesch Reading Ease of privacy policies are calculated. Average Reading Grade Level of privacy policies is 8.5. It is slightly greater than average adult RGL in the US. Free mHealth apps have a large number of users in other, less educated parts of the World. Privacy policies with an average RGL of 8.5 may be difficult to comprehend in less educated populations.

In this paper, we proposed a framework to evaluate information retrieval systems in presence of multidimensional relevance. This is an important problem in tasks such as consumer health search, where the understandability and trustworthiness of information greatly influence people's decisions based on the search engine results, but common topicality-only evaluation measures ignore these aspects. We used synthetic and real data to compare our proposed framework, named MM, to the understandability-biased information evaluation (UBIRE), an existing framework used in the context of consumer health search. We showed how the proposed approach diverges from the UBIRE framework, and how MM can be used to better understand the trade-offs between topical relevance and the other relevance dimensions.

Online privacy policies notify users of a Website how their personal information is collected, processed and stored. Against the background of rising privacy concerns, privacy policies seem to represent an influential instrument for increasing customer trust and loyalty. However, in practice, consumers seem to actually read privacy policies only in rare cases, possibly reflecting the common assumption stating that policies are hard to comprehend. By designing and implementing an automated extraction and readability analysis toolset that embodies a diversity of established readability measures, we present the first large-scale study that provides current empirical evidence on the readability of nearly 50,000 privacy policies of popular English-speaking Websites. The results empirically confirm that on average, current privacy policies are still hard to read. Furthermore, this study presents new theoretical insights for readability research, in particular, to what extent practical readability measures are correlated. Specifically, it shows the redundancy of several well-established readability metrics such as SMOG, RIX, LIX, GFI, FKG, ARI, and FRES, thus easing future choice making processes and comparisons between readability studies, as well as calling for research towards a readability measures framework. Moreover, a more sophisticated privacy policy extractor and analyzer as well as a solid policy text corpus for further research are provided.