Various technologies have been developed to help make the world more accessible to visually impaired people, and recent advances in low-cost wearable and mobile computing are likely to drive even more advances. However, the unique privacy and security needs of visually impaired people remain largely unaddressed. We conducted an exploratory user study with 14 visually impaired participants to understand the techniques they currently use for protecting privacy, their remaining privacy concerns, and how new technologies may be able to help. The interviews explored privacy not only in the physical world (e.g., bystanders overhearing private conversations) and the online world (e.g., determining if a URL is legitimate), but also in the interface between the two (e.g. bystanders 'shoulder-surfing' data from screens). The study revealed serious concerns that are not adequately solved by current technology, and suggested new directions for improving the privacy of this significant fraction of the population.

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Smartphone apps today request permission to access a multitude of sensitive resources, which users must accept completely during installation (e.g., on Android) or selectively configure after installation (e.g., on iOS, but also planned for Android). Everyday users, however, do not have the ability to make informed decisions about which permissions are essential for their usage. For enhanced privacy, we seek to leverage crowdsourcing to find minimal sets of permissions that will preserve the usability of the app for diverse users.We advocate an efficient 'lattice-based' crowd-management strategy to explore the space of permissions sets. We conducted a user study (N = 26) in which participants explored different permission sets for the popular Instagram app. This study validates our efficient crowd management strategy and shows that usability scores for diverse users can be predicted accurately, enabling suitable recommendations.

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Aim The correlation of subclinical hypothyroidism (SCH) and polycystic ovary syndrome (PCOS) is a still insufficiently explored entity. The aim of this study was to determine the correlation between SCH and PCOS along with the impact of SCH on metabolic and hormonal parameters in women with PCOS. Methodology This cross-sectional study was conducted at the Gynecology Outpatient Department of Ziauddin Hospital Kemari, Karachi, Pakistan, from June 2019 to December 2019. A total of 90 diagnosed cases of PCOS were enrolled in the study. A non-probability consecutive sampling technique was used. After taking informed consent, participants were evaluated through clinical interviews, a questionnaire, and anthropometric measurements. The participants underwent the following assessments, i.e., transabdominal ultrasonography, hormonal profile (free testosterone, follicle-stimulating hormone, luteinizing hormone), and fasting blood sugar. Participants were divided into two groups based on thyroid-stimulating hormone (TSH) into the euthyroid group and subclinical hypothyroid (SCH) group. The Mann-Whitney test was used for comparing the two groups. Results Our results showed a significant difference in weight, body mass index (BMI), insulin, homeostatic model assessment of insulin resistance (HOMA-IR), and TSH were found in the SCH group as compared to the euthyroid group. A significant correlation of TSH with waist-hip ratio (WHR), weight, body mass index (BMI), insulin, and the homeostatic model assessment of insulin resistance (HOMA-IR) in PCOS patients. Conclusion This study showed a significant correlation of subclinical hypothyroidism with polycystic ovary syndrome. We found subclinical hypothyroidism may aggravate the insulin resistance; therefore, PCOS patients must be screened with a thyroid profile.