Mobile Privacy and Business-to-Platform Dependencies: An Analysis of SEC Disclosures

This Article systematically examines the dependence of mobile apps on mobile platforms for the collection and use of personal information through an analysis of Securities and Exchange Commission (SEC) filings of mobile app companies. The Article uses these disclosures to find systematic evidence of how app business models are shaped by the governance of user data by mobile platforms, in order to reflect on the role of platforms in privacy regulation more generally. The analysis of SEC filings documented in the Article produces new and unique insights into the data practices and data-related aspects of the business models of popular mobile apps and shows the value of SEC filings for privacy law and policy research more generally. The discussion of SEC filings and privacy builds on regulatory developments in SEC disclosures and cybersecurity of the last decade. The Article also connects to recent regulatory developments in the U.S. and Europe, including the General Data Protection Regulation, the proposals for a new ePrivacy Regulation and a Regulation of fairness in business-to-platform relations

CAMEO Stakeholders Report

Computer-Aided Management of Emergency Operations (CAMEO) is a suite of software applications used to plan for and respond to chemical emergencies. CAMEO was first released in 1986, and was jointly developed by the U.S. Environmental Protection Agency (US EPA) and the National Oceanic and Atmospheric Administration (NOAA) to assist front-line chemical emergency planners and responders. It has since undergone numerous modification and upgrades, and is a critical tool used today for chemical spills, other hazards, and emergency management. The CAMEO system integrates a chemical database and a method to manage the data, an air dispersion model, and a mapping capability. All modules work interactively to share and display critical information in a timely fashion. As a result of fatal chemical accidents in recent years, Executive Order (EO) 13650 (Improving Chemical Facility Safety and Security) was signed on August 1, 2013 for: Improving Operational Coordination with State, Local and Tribal partners Enhancing Federal Coordination Enhancing Information Collection and Sharing Modernizing Regulations, Guidance, Policy and Standards Identifying Best Practices. The CAMEO team has been working to address these EO requirements and the areas of action in a manner that will best meet the needs of CAMEO users and stakeholders

Crime and Social media

Purpose-The study complements the scant macroeconomic literature on the development outcomes of social media by examining the relationship between Facebook penetration and violent crime levels in a cross-section of 148 countries for the year 2012. Design/methodology/approach-The empirical evidence is based on Ordinary Least Squares (OLS), Tobit and Quantile regressions. In order to respond to policy concerns on the limited evidence on the consequences of social media in developing countries, the dataset is disaggregated into regions and income levels. The decomposition by income levels included: low income, lower middle income, upper middle income and high income. The corresponding regions include: Europe and Central Asia, East Asia and the Pacific, Middle East and North Africa, Sub-Saharan Africa and Latin America. Findings-From OLS and Tobit regressions, there is a negative relationship between Facebook penetration and crime. However, Quantile regressions reveal that the established negative relationship is noticeable exclusively in the 90th crime decile. Further, when the dataset is decomposed into regions and income levels, the negative relationship is evident in the Middle East and North Africa (MENA) while a positive relationship is confirmed for sub-Saharan Africa. Policy implications are discussed. Originality/value- Studies on the development outcomes of social media are sparse because of a lack of reliable macroeconomic data on social media. This study primarily complemented three existing studies that have leveraged on a newly available dataset on Facebook

Privacy in crowdsourcing:a systematic review

The advent of crowdsourcing has brought with it multiple privacy challenges. For example, essential monitoring activities, while necessary and unavoidable, also potentially compromise contributor privacy. We conducted an extensive literature review of the research related to the privacy aspects of crowdsourcing. Our investigation revealed interesting gender differences and also differences in terms of individual perceptions. We conclude by suggesting a number of future research directions.</p

Adaptive cancelation of self-generated sensory signals in a whisking robot

Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the often-used tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme

Privacy through uncertainty in location-based services

Location-Based Services (LBS) are becoming more prevalent. While there are many benefits, there are also real privacy risks. People are unwilling to give up the benefits - but can we reduce privacy risks without giving up on LBS entirely? This paper explores the possibility of introducing uncertainty into location information when using an LBS, so as to reduce privacy risk while maintaining good quality of service. This paper also explores the current uses of uncertainty information in a selection of mobile applications