scholarly journals When Private Information Settles the Bill: Money and Privacy in Google’s Market for Smartphone Applications

2019 ◽  
Vol 65 (8) ◽  
pp. 3470-3494 ◽  
Author(s):  
Michael Kummer ◽  
Patrick Schulte
Keyword(s):  
Private Information ◽  
Personal Information ◽  
Supply And Demand ◽  
User Group ◽  
Smartphone Applications ◽  
Difference In Differences ◽  
Android Market ◽  
Using Data ◽  
Google Play ◽  
Shed Light

We shed light on a money-for-privacy trade-off in the market for smartphone applications (“apps”). Developers offer their apps at lower prices in return for greater access to personal information, and consumers choose between low prices and more privacy. We provide evidence for this pattern using data from 300,000 apps obtained from the Google Play Store (formerly Android Market) in 2012 and 2014. Our findings show that the market’s supply and demand sides both consider an app’s ability to collect private information, measured by the apps’s use of privacy-sensitive permissions: (1) cheaper apps use more privacy-sensitive permissions; (2) given price and functionality, demand is lower for apps with sensitive permissions; and (3) the strength of this relationship depends on contextual factors, such as the targeted user group, the app’s previous success, and its category. Our results are robust and consistent across several robustness checks, including the use of panel data, a difference-in-differences analysis, “twin” pairs of apps, and various measures of privacy-sensitivity and app demand. This paper was accepted by Anandhi Bharadwaj, information systems.

scholarly journals P50 Exploring the Merits and Availability of Smartphone Applications Related to Melanoma

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
Jade Kabbani ◽  
Jamil Kabbani ◽  
Jade Kabbani
Keyword(s):  
Clinical Management ◽  
Literature Search ◽  
Patient Access ◽  
Smartphone Applications ◽  
App Store ◽  
Widespread Availability ◽  
Smartphone Use ◽  
Clinical Tools ◽  
Google Play ◽  
Privacy Issues

Abstract Background The increased use of smartphone applications across healthcare specialties has been particularly relevant in dermatology, with dermatology related applications widely available on mainstream application stores. We reviewed published literature regarding melanoma-related applications, and the number and types of such applications available for download. Methods A literature search of “dermatology”, “smartphone” and “melanoma” was conducted to identify publications assessing applications of interest. “Melanoma” was searched in Apple’s (iOS) “App Store” and Google’s “Google Play”, and application purposes and ratings were analysed. Results 54 of the 63 literature search results explored smartphone use in relation to melanoma, describing benefits including quicker patient access to care, reduced referrals and hence unnecessary consultations, and improved accessibility to information. However, concerns include insufficient image quality, privacy issues related to encryption, and diagnostic inaccuracy. Searches on the Google Play and iOS stores identified 249 and 51 apps respectively. 25% of Google Play results were categorised as clinical tools, 17% as educational, and 58% as recreational. The corresponding results for the App store were 92%, 6% and 2%. 81% of the educational apps and 92% of the clinical management apps related to dermatology and melanoma on Google Play, whereas all of the clinical management apps and 67% of the education apps on the App store were of relevance. Conclusion The results illustrate the widespread availability of applications related to melanoma, particularly for educational and clinical purposes. Standardising photographing techniques, improving diagnostic accuracy, and privacy issues are important aspects to consider and warrant further investigation.

AI and privacy concerns: a smart meter case study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jillian Carmody ◽  
Samir Shringarpure ◽  
Gerhard Van de Venter
Keyword(s):  
Private Information ◽  
Personal Information ◽  
Consumer Information ◽  
Data Sets ◽  
Smart Meter ◽  
Smart Meters ◽  
Content Type ◽  
Privacy Concerns ◽  
Privacy Legislation

Purpose The purpose of this paper is to demonstrate privacy concerns arising from the rapidly increasing advancements and use of artificial intelligence (AI) technology and the challenges of existing privacy regimes to ensure the on-going protection of an individual’s sensitive private information. The authors illustrate this through a case study of energy smart meters and suggest a novel combination of four solutions to strengthen privacy protection. Design/methodology/approach The authors illustrate how, through smart meter obtained energy data, home energy providers can use AI to reveal private consumer information such as households’ electrical appliances, their time and frequency of usage, including number and model of appliance. The authors show how this data can further be combined with other data to infer sensitive personal information such as lifestyle and household income due to advances in AI technologies. Findings The authors highlight data protection and privacy concerns which are not immediately obvious to consumers due to the capabilities of advanced AI technology and its ability to extract sensitive personal information when applied to large overlapping granular data sets. Social implications The authors question the adequacy of existing privacy legislation to protect sensitive inferred consumer data from AI-driven technology. To address this, the authors suggest alternative solutions. Originality/value The original value of this paper is that it illustrates new privacy issues brought about by advances in AI, failings in current privacy legislation and implementation and opens the dialog between stakeholders to protect vulnerable consumers.

scholarly journals Information Privacy Concerns Among Instagram Users: The Case of Indonesian College Students

2020 ◽  
Vol 6 (2) ◽  
pp. 159
Author(s):  
Eko Wahyu Tyas Darmaningrat ◽  
Hanim Maria Astuti ◽  
Fadhila Alfi
Keyword(s):  
College Students ◽  
Private Information ◽  
Structural Equation ◽  
Personal Information ◽  
Information Privacy ◽  
Risk Awareness ◽  
User Privacy ◽  
Privacy Concerns ◽  
Information Privacy Concerns ◽  
Increased Risk

Background: Teenagers in Indonesia have an open nature and satisfy their desire to exist by uploading photos or videos and writing posts on Instagram. The habit of uploading photos, videos, or writings containing their personal information can be dangerous and potentially cause user privacy problems. Several criminal cases caused by information misuse have occurred in Indonesia.Objective: This paper investigates information privacy concerns among Instagram users in Indonesia, more specifically amongst college students, the largest user group of Instagram in Indonesia.Methods: This study referred to the Internet Users' Information Privacy Concerns (IUIPC) method by collecting data through the distribution of online questionnaires and analyzed the data by using Structural Equation Modelling (SEM).Results: The research finding showed that even though students are mindful of the potential danger of information misuse in Instagram, it does not affect their intention to use Instagram. Other factors that influence Indonesian college students' trust are Instagram's reputation, the number of users who use Instagram, the ease of using Instagram, the skills and knowledge of Indonesian students about Instagram, and the privacy settings that Instagram has.Conclusion: The awareness and concern of Indonesian college students for information privacy will significantly influence the increased risk awareness of information privacy. However, the increase in risk awareness does not directly affect Indonesian college students' behavior to post their private information on Instagram.

scholarly journals Osteoporosis Risk Prediction Using Data Mining Algorithms

2020 ◽  
Author(s):  
Efat Jabarpour ◽  
Amin Abedini ◽  
Abbasali Keshtkar
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
Industry Standard ◽  
Disease Information ◽  
Increased Risk ◽  
Using Data ◽  
Mining Algorithms

Introduction: Osteoporosis is a disease that reduces bone density and loses the quality of bone microstructure leading to an increased risk of fractures. It is one of the major causes of inability and death in elderly people. The current study aims at determining the factors influencing the incidence of osteoporosis and providing a predictive model for the disease diagnosis to increase the diagnostic speed and reduce diagnostic costs. Methods: An Individual's data including personal information, lifestyle, and disease information were reviewed. A new model has been presented based on the Cross-Industry Standard Process CRISP methodology. Besides, Support Vector Machine (SVM) and Bayes methods (Tree Augmented Naïve Bayes (TAN)) and Clementine12 have been used as data mining tools. Results: Some features have been detected to affect this disease. The rules have been extracted that can be used as a pattern for the prediction of the patients' status. Classification precision was calculated to be 88.39% for SVM, and 91.29% for  (TAN) when the precision of  TAN  is higher comparing to other methods. Conclusion: The most effective factors concerning osteoporosis are detected and can be used for a new sample with defined characteristics to predict the possibility of osteoporosis in a person.  

scholarly journals Application of Machine Learning Algorithms to Depression Screening and Attempt at Pattern Extraction of Patient-Reported Outcomes that Negatively Affect Classification Accuracy (Preprint)

2017 ◽  
Author(s):  
Junetae Kim ◽  
Byungtae Lee ◽  
Sae Byul Lee ◽  
Il Yong Chung ◽  
Sei Hyun Ahn ◽  
...  
Keyword(s):  
Deep Learning ◽  
Missing Data ◽  
Patient Reported Outcomes ◽  
Machine Learning Algorithms ◽  
Depression Screening ◽  
Smartphone Applications ◽  
Patient Reported ◽  
Negative Effect ◽  
Using Data ◽  
Screening Accuracy

BACKGROUND Smartphone applications have recently been used as a breakthrough technology for monitoring mental health conditions in cancer outpatient settings. However, the use of electronic patient-reported outcomes (ePROs) on mental conditions through smartphone applications raises new concerns, which includes the question of the accuracy of depression screening. Thus, research is essential for improving the depression-screening performance. OBJECTIVE This study aims to (1) test whether deep-learning-based algorithms can overcome the limitations of traditional statistical methods in terms of depression screening accuracy. In addition, the study aims to (2) explore ePRO patterns that adversely affect depression screening accuracy. METHODS As a deep learning-based algorithm, a feedforward neural network algorithm was used. As a traditional statistical method, a random intercept logistic regression was employed. To explore the ePRO patterns that negatively impact model accuracy, mental fluctuations, missing data, and compounding effects between mental fluctuations and missing data were tested. The performances of the algorithms and the effects of the ePRO patterns were measured through the receiver operating characteristic comparison test. RESULTS The results of the study show that the performance of the deep-learning-based models was superior to that of the traditional statistical approach. The study found that mental fluctuations statistically reduced the accuracy of depression-screening models. A weak association between ePRO omissions and screening accuracy was found. Moreover, the compounding effects that had a negative effect on the depression screening accuracy were statistically significant. CONCLUSIONS Although well-trained deep-learning-based models exhibit excellent performance, they still have some limitations. Thus, it is very important to focus on data quality to predict health outcomes when using data that is difficult to quantify, such as mental conditions.

Do Country-level Creditor Protections Affect Firm-level Debt Structure Concentration?

2021 ◽  
Author(s):  
Kose John ◽  
Mahsa S Kaviani ◽  
Lawrence Kryzanowski ◽  
Hosein Maleki
Keyword(s):  
Debt Structure ◽  
Difference In Differences ◽  
Firm Level ◽  
Creditor Rights ◽  
Country Level ◽  
Bankruptcy Costs ◽  
Cash Flow Volatility ◽  
Cross Country ◽  
Using Data ◽  
Alternative Settings

Abstract We study the effects of country-level creditor protections on the firm-level choice of debt structure concentration. Using data from 46 countries, we show that firms form more concentrated debt structures in countries with stronger creditor protection. We propose a trade-off framework of optimal debt structure and show that in strong creditor rights regimes, the benefit of forming concentrated structures outweighs its cost. Because strong creditor protections increase liquidation bias, firms choose concentrated debt structures to improve the probability of successful distressed debt renegotiations. Firms with ex-ante higher bankruptcy costs, including those with higher intangibility, cash flow volatility, R&D expenses, and leverage exhibit stronger effects. Firms with restricted access to capital are also affected more. A difference-in-differences analysis of firms’ debt structure responses to creditor rights reforms confirms the cross-country results. Our findings are robust to alternative settings and a battery of robustness checks.

Next Generation E-Government

2014 ◽  
pp. 124-146
Author(s):  
Maria Moloney ◽  
Gary Coyle
Keyword(s):  
Private Information ◽  
Personal Information ◽  
Future Internet ◽  
The Other ◽  
Digital Society ◽  
The Public ◽  
Traditional Sense ◽  
Open Discussion ◽  
The One ◽  
Evolving Model

The evolving model of the Future Internet has, at its heart, the users of the Internet. Web 2.0 and Government 2.0 initiatives help citizens communicate even better with their governments. Such initiatives have the potential to empower citizens by giving them a stronger voice in both the traditional sense and in the digital society. Pressure is mounting on governments to listen to the voice of the public expressed through these technologies and incorporate their needs into public policy. On the other hand, governments still have a duty to protect their citizens' personal information against unlawful and malicious intent. This responsibility is essential to any government in an age where there is an increasing burden on citizens to interact with governments via electronic means. This chapter examines this dual agenda of modern governments to engage with its citizens, on the one hand, to encourage transparency and open discussion, and to provide digitally offered public services that require the protection of citizens' private information, on the other. In this chapter, it is argued that a citizen-centric approach to online privacy protection that works in tandem with the open government agenda will provide a unified mode of interaction between citizens, businesses, and governments in digital society.

School Districts Stumbled on Data Privacy

2012 ◽  
pp. 12-15 ◽  
Author(s):  
Irene Chen
Keyword(s):  
Public Schools ◽  
School Districts ◽  
Middle Schools ◽  
Social Security ◽  
Private Information ◽  
Web Site ◽  
Data Privacy ◽  
Personal Information ◽  
Personal Data ◽  
Short Period

The story describes how three school institutes are grappling with the loss of private information, each through a unique set of circumstances. Pasadena City Public Schools discovered that it had sold several computers containing the names and Social Security numbers of employees as surplus. Stephens Public Schools learned that personal information about students at one of its middle schools was lost when a bag containing a thumb drive was stolen. Also, Woodlands Public Schools accidentally exposed employee personal data on a public Web site for a short period of time. How should each of the institutes react?

Malicious Application Detection and Classification System for Android Mobiles

2020 ◽  
pp. 122-142
Author(s):  
Sapna Malik ◽  
Kiran Khatter
Keyword(s):  
Machine Learning ◽  
Classification System ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Android Applications ◽  
Android Market ◽  
Malicious Apps ◽  
Android Permission ◽  
Google Play

The Android Mobiles constitute a large portion of mobile market which also attracts the malware developer for malicious gains. Every year hundreds of malwares are detected in the Android market. Unofficial and Official Android market such as Google Play Store are infested with fake and malicious apps which is a warning alarm for naive user. Guided by this insight, this paper presents the malicious application detection and classification system using machine learning techniques by extracting and analyzing the Android Permission Feature of the Android applications. For the feature extraction, the authors of this work have developed the AndroData tool written in shell script and analyzed the extracted features of 1060 Android applications with machine learning algorithms. They have achieved the malicious application detection and classification accuracy of 98.2% and 87.3%, respectively with machine learning techniques.

Public Intimacy and the New Face (Book) of Surveillance

2010 ◽  
pp. 2057-2068
Author(s):  
Lemi Baruh ◽  
Levent Soysal
Keyword(s):  
Social Media ◽  
Private Information ◽  
Personal Information ◽  
Interactive Media ◽  
Social Currency ◽  
Heated Debate ◽  
The Relationship

In recent years, social media have become an important avenue for self-expression. At the same time, the ease with which individuals disclose their private information has added to an already heated debate about the privacy implications of interactive media. This chapter investigates the relationship between disclosure of personal information in social media and two related trends: the increasing value of subjective or private experience as a social currency and the evolving nature of automated dataveillance. The authors argue that the results of the extended ability of individuals to negotiate their identity through social media are contradictory. The information revealed to communicate the complexity of one’s identity becomes an extensive source of data about individuals, thereby contributing to the functioning of a new regime of surveillance.

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