Mathematical Reconciliation of Medical Privacy Policies

2020 ◽  
Vol 12 (1) ◽  
pp. 1-18
Author(s):  
Dinesha Ranathunga ◽  
Matthew Roughan ◽  
Hung Nguyen
Keyword(s):  
Privacy Policies ◽  
Medical Privacy

HIPAA and medical privacy: Status of the lawsuit

2003 ◽  
Author(s):  
Dave Byrom ◽  
Patricia Dowds
Keyword(s):  
Medical Privacy

Problematic Privacy Policies of Voice Assistant Applications

2021 ◽  
pp. 2-9
Author(s):  
Song Liao ◽  
Christin Wilson ◽  
Cheng Long ◽  
Hongxin Hu ◽  
Huixing Deng
Keyword(s):  
Privacy Policies

The New Federal Medical-Privacy Rule

2002 ◽  
Vol 347 (15) ◽  
pp. 1133-1134 ◽  
Author(s):  
Jennifer Kulynych ◽  
David Korn
Keyword(s):  
Privacy Rule ◽  
Medical Privacy

Evaluating Mobile Apps For Dietary and Food Timing Assessment For Use in Research and Clinical Assessment (Preprint)

2021 ◽  
Author(s):  
Siena Gioia ◽  
Irma M Vlassac ◽  
Demsina Babazadeh ◽  
Noah L Fryou ◽  
Elizabeth Do ◽  
...  
Keyword(s):  
Dietary Intake ◽  
Keyword Search ◽  
Mobile Apps ◽  
Dietary Assessment ◽  
Registered Dietitian ◽  
Food Record ◽  
User Privacy ◽  
Privacy Policies ◽  
Dietary Record ◽  
System Usability Scale

UNSTRUCTURED Abstract: Over the last decade, health apps have become an increasingly popular tool utilized by clinicians and researchers to track food consumption and exercise. However, as consumer apps have primarily focused on tracking dietary intake and exercise, many lack technological features to facilitate the capture of critical food timing details. To determine a viable app that recorded both dietary intake and food timing for use in our clinical study, we evaluated the timestamp data, usability, privacy policies, accuracy of nutrient estimates, and general features of 11 mobile apps for dietary assessment. Apps were selected using a keyword search of related terms and the following apps were reviewed: Bitesnap, Cronometer, DiaryNutrition, DietDiary, FoodDiary, FoodView, Macros, MealLogger, myCircadianClock, MyFitnessPal, and MyPlate. Our primary goal was identifying apps that record food timestamps, which 8 of the reviewed apps did (73%). Of those, only 4/11 (36%) allowed users to edit the timestamps, an important feature. Next, we sought to evaluate the usability of the apps, using the System Usability Scale (SUS) across 2 days, with 82% of the apps receiving favorable scores for usability (9/11 apps). To enable use in research and clinic settings, the privacy policies of each app were systematically reviewed using common criteria with 1 Health Insurance Portability and Accountability Act (HIPAA) compliant app (Cronometer). Furthermore, protected health information is collected by 9/11 (81%) of the apps. Lastly, to assess the accuracy of nutrient estimates generated by these apps, we selected 4 sample food items and one researcher’s 3-day dietary record to input into each app. The caloric and macronutrient estimates of the apps were compared to nutrient estimates provided by a registered dietitian using the Nutrition Data System for Research (NDSR). Compared to the 3-day food record, the apps were found to consistently underestimate calories and macronutrients compared to NDSR. Overall, we find the Bitesnap app to provide flexible dietary and food timing functionality capable for research or clinical use with the majority of apps lacking in necessary food timing functionality or user privacy.

Automated Extraction and Presentation of Data Practices in Privacy Policies

2021 ◽  
Vol 2021 (2) ◽  
pp. 88-110
Author(s):  
Duc Bui ◽  
Kang G. Shin ◽  
Jong-Min Choi ◽  
Junbum Shin
Keyword(s):  
User Study ◽  
Personal Information ◽  
Personal Data ◽  
Neural Model ◽  
Automated Analysis ◽  
Entity Recognition ◽  
Automated System ◽  
Privacy Policies ◽  
Fine Grained ◽  
Data Practices

Abstract Privacy policies are documents required by law and regulations that notify users of the collection, use, and sharing of their personal information on services or applications. While the extraction of personal data objects and their usage thereon is one of the fundamental steps in their automated analysis, it remains challenging due to the complex policy statements written in legal (vague) language. Prior work is limited by small/generated datasets and manually created rules. We formulate the extraction of fine-grained personal data phrases and the corresponding data collection or sharing practices as a sequence-labeling problem that can be solved by an entity-recognition model. We create a large dataset with 4.1k sentences (97k tokens) and 2.6k annotated fine-grained data practices from 30 real-world privacy policies to train and evaluate neural networks. We present a fully automated system, called PI-Extract, which accurately extracts privacy practices by a neural model and outperforms, by a large margin, strong rule-based baselines. We conduct a user study on the effects of data practice annotation which highlights and describes the data practices extracted by PI-Extract to help users better understand privacy-policy documents. Our experimental evaluation results show that the annotation significantly improves the users’ reading comprehension of policy texts, as indicated by a 26.6% increase in the average total reading score.

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