scholarly journals Platform Revenue Strategy Selection Considering Consumer Group Data Privacy Regulation

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2904
Author(s):  
Xudong Lin ◽  
Shuilin Liu ◽  
Xiaoli Huang ◽  
Hanyang Luo ◽  
Sumin Yu
Keyword(s):  
Social Welfare ◽  
Data Privacy ◽  
Threshold Value ◽  
Market Regulation ◽  
Consumer Group ◽  
Privacy Regulation ◽  
Service Matching ◽  
Group Data ◽  
Medium Level ◽  
Revenue Efficiency

In the era of big data, consumer group privacy has become an important source of revenue for the digital platform. Considering the situation that the platform collects consumer group data privacy to generate business revenue, we explore how the service matching level and commission rate affect the platform revenue, social welfare, and seller benefits. Based on the theory of group privacy, the three-party equilibrium evolution is solved by constructing a sequential game model including platform, seller, and consumer alliance. It is found that when the service matching level of the platform is greater than the threshold value, there are two main situations: on the one hand, if using the data privacy of a consumer group is subject to market regulation, the platform will set a high commission rate and service matching level in order to maximize profit. However, social welfare and seller’s business benefit both reach a minimum in this case, and the three-party game cannot attain equilibrium. On the other hand, when the market governor relaxes the platform’s regulation on the use of consumer group privacy data and data revenue efficiency is high enough, the platform can maximize the revenue by increasing the service matching level and reducing the commission rate. The optimal commission rate depends on the data revenue efficiency of the platform. Moreover, when the platform sets the highest commission rate and the service matching level is at a medium level, a stable partial equilibrium among the three-party will be achieved. These conclusions can give some insights into platform’s business model choice decision.

scholarly journals Social Welfare Analysis under Different Levels of Consumers’ Privacy Regulation

2021 ◽  
Vol 16 (7) ◽  
pp. 2943-2964
Author(s):  
Xudong Lin ◽  
Xiaoli Huang ◽  
Shuilin Liu ◽  
Yulin Li ◽  
Hanyang Luo ◽  
...  
Keyword(s):  
Social Welfare ◽  
Information Sharing ◽  
Privacy Protection ◽  
Rapid Development ◽  
Specific Information ◽  
Privacy Regulation ◽  
Privacy Preference ◽  
The Cost ◽  
The Impact ◽  
Different Levels

With the rapid development of information technology, digital platforms can collect, utilize, and share large amounts of specific information of consumers. However, these behaviors may endanger information security, thus causing privacy concerns among consumers. Considering the information sharing among firms, this paper constructs a two-period duopoly price competition Hotelling model, and gives insight into the impact of three different levels of privacy regulations on industry profit, consumer surplus, and social welfare. The results show that strong privacy protection does not necessarily make consumers better off, and weak privacy protection does not necessarily hurt consumers. Information sharing among firms will lead to strong competitive effects, which will prompt firms to lower the price for new customers, thus damaging the profits of firms, and making consumers’ surplus higher. The level of social welfare under different privacy regulations depends on consumers’ product-privacy preference, and the cost of information coordination among firms. With the cost of information coordination among firms increasing, it is only in areas where consumers have greater privacy preferences that social welfare may be optimal under the weak regulation.

scholarly journals Bought and Sold: Exploring the Effects of Big Data on User Agency and Commodification

2021 ◽  
Author(s):  
Kristia M. Pavlakos
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Privacy ◽  
Large Data ◽  
Data Sets ◽  
Privacy Regulation ◽  
Scholarly Literature ◽  
User Interests ◽  
The Social ◽  
The Relationship

Big Data1is a phenomenon that has been increasingly studied in the academy in recent years, especially in technological and scientific contexts. However, it is still a relatively new field of academic study; because it has been previously considered in mainly technological contexts, more attention needs to be drawn to the contributions made in Big Data scholarship in the social sciences by scholars like Omar Tene and Jules Polonetsky, Bart Custers, Kate Crawford, Nick Couldry, and Jose van Dijk. The purpose of this Major Research Paper is to gain insight into the issues surrounding privacy and user rights, roles, and commodification in relation to Big Data in a social sciences context. The term “Big Data” describes the collection, aggregation, and analysis of large data sets. While corporations are usually responsible for the analysis and dissemination of the data, most of this data is user generated, and there must be considerations regarding the user’s rights and roles. In this paper, I raise three main issues that shape the discussion: how users can be more active agents in data ownership, how consent measures can be made to actively reflect user interests instead of focusing on benefitting corporations, and how user agency can be preserved. Through an analysis of social sciences scholarly literature on Big Data, privacy, and user commodification, I wish to determine how these concepts are being discussed, where there have been advancements in privacy regulation and the prevention of user commodification, and where there is a need to improve these measures. In doing this, I hope to discover a way to better facilitate the relationship between data collectors and analysts, and user-generators. 1 While there is no definitive resolution as to whether or not to capitalize the term “Big Data”, in capitalizing it I chose to conform with such authors as boyd and Crawford (2012), Couldry and Turow (2014), and Dalton and Thatcher (2015), who do so in the scholarly literature.

scholarly journals Why deservingness theory needs qualitative research: Comparing focus group discussions on social welfare in three welfare regimes

2019 ◽  
Vol 60 (3) ◽  
pp. 190-216 ◽  
Author(s):  
Tijs Laenen ◽  
Federica Rossetti ◽  
Wim van Oorschot
Keyword(s):  
Qualitative Research ◽  
United Kingdom ◽  
Focus Group ◽  
Social Welfare ◽  
Research Field ◽  
Welfare Regimes ◽  
Group Data ◽  
Focus Group Data ◽  
Financial Need ◽  
Normative Criteria

This article argues that the ever-growing research field of welfare deservingness is in need of qualitative research. Using focus group data collected in Denmark, Germany, and the United Kingdom, we show that citizens discussing matters of social welfare make explicit reference not only to the deservingness criteria of control, reciprocity, and need but also to a number of context-related criteria extending beyond the deservingness framework (e.g. equality/universalism). Furthermore, our findings suggest the existence of an institutional logic to welfare preferences, as the focus group participants to a large extent echoed the normative criteria that are most strongly embedded in the institutional structure of their country’s welfare regime. Whereas financial need is the guiding criterion in the “liberal” United Kingdom, reciprocity is dominant in “corporatist-conservative” Germany. In “social-democratic” Denmark, it appears impossible to single out one dominant normative criterion. Instead, the Danish participants seem torn between the criteria of need, reciprocity, and equality/universalism.

scholarly journals Tool Wear Detection Based on Duffing-Holmes Oscillator

2008 ◽  
Vol 2008 ◽  
pp. 1-15 ◽  
Author(s):  
Wanqing Song ◽  
Shen Deng ◽  
Jianguo Yang ◽  
Qiang Cheng
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Golden Section ◽  
Threshold Value ◽  
Time Sequence ◽  
Step Size ◽  
Computation Cost ◽  
Tool Condition ◽  
Group Data ◽  
Short Time

The cutting sound in the audible range includes plenty of tool wear information. The sound is sampled by the acoustic emission (AE) sensor as a short-time sequence, then worn wear can be detected by the Duffing-Holmes oscillator. A novel engineering method is proposed for determining the chaotic threshold of the Duffing-Holmes oscillator. First, a rough threshold value is calculated by local Lyapunov exponents with a step size 0.1. Second, the exact threshold value is calculated by the Duffing-Holmes system in terms of the law of the golden section. The advantage of the method is low computation cost. The feasibility for tool condition detection is demonstrated by the 27 kinds of cutting conditions with sharp tool and worn tool in turning experiments. The 54 group data sampled as noisy are embedded into the Duffing-Holmes oscillator, respectively. Finally, one chaotic threshold is determined conveniently which can distinguish between worn tool or sharp tool.

scholarly journals Bought and Sold: Exploring the Effects of Big Data on User Agency and Commodification

2021 ◽  
Author(s):  
Kristia M. Pavlakos
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Privacy ◽  
Large Data ◽  
Data Sets ◽  
Privacy Regulation ◽  
Scholarly Literature ◽  
User Interests ◽  
The Social ◽  
The Relationship

Big Data1is a phenomenon that has been increasingly studied in the academy in recent years, especially in technological and scientific contexts. However, it is still a relatively new field of academic study; because it has been previously considered in mainly technological contexts, more attention needs to be drawn to the contributions made in Big Data scholarship in the social sciences by scholars like Omar Tene and Jules Polonetsky, Bart Custers, Kate Crawford, Nick Couldry, and Jose van Dijk. The purpose of this Major Research Paper is to gain insight into the issues surrounding privacy and user rights, roles, and commodification in relation to Big Data in a social sciences context. The term “Big Data” describes the collection, aggregation, and analysis of large data sets. While corporations are usually responsible for the analysis and dissemination of the data, most of this data is user generated, and there must be considerations regarding the user’s rights and roles. In this paper, I raise three main issues that shape the discussion: how users can be more active agents in data ownership, how consent measures can be made to actively reflect user interests instead of focusing on benefitting corporations, and how user agency can be preserved. Through an analysis of social sciences scholarly literature on Big Data, privacy, and user commodification, I wish to determine how these concepts are being discussed, where there have been advancements in privacy regulation and the prevention of user commodification, and where there is a need to improve these measures. In doing this, I hope to discover a way to better facilitate the relationship between data collectors and analysts, and user-generators. 1 While there is no definitive resolution as to whether or not to capitalize the term “Big Data”, in capitalizing it I chose to conform with such authors as boyd and Crawford (2012), Couldry and Turow (2014), and Dalton and Thatcher (2015), who do so in the scholarly literature.

Sequencing, Layering, and Feedbacks in Global Regulation

2016 ◽  
Author(s):  
Abraham L. Newman
Keyword(s):  
International Organizations ◽  
Data Privacy ◽  
Historical Institutionalism ◽  
Global Politics ◽  
International Affairs ◽  
Informal Networks ◽  
Global Regulation ◽  
Privacy Regulation ◽  
Domestic Law ◽  
Cross National

From banking standards to data privacy, regulation has entered the lexicon of international affairs. Unlike trade or currencies, however, there are few formal treaty-based international organizations resolving disputes or setting the rules for the world. Instead, global regulation is frequently shaped by informal networks of regulators or at times by the extraterritorial extension of domestic law by large markets. Drawing on work from historical institutionalism, this chapter argues that the global politics of regulation is in important respects the product of domestic and international institutions interacting over time and across space. In developing three mechanisms—relative sequencing, cross-national layering, and transnational feedbacks —the chapter argues that historical institutionalism helps address lacunae in extant approaches to global regulation.

scholarly journals D2D Big Data Privacy-Preserving Framework Based on (a, k)-Anonymity Model

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Jie Wang ◽  
Hongtao Li ◽  
Feng Guo ◽  
Wenyin Zhang ◽  
Yifeng Cui
Keyword(s):  
Big Data ◽  
Private Information ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Computing Time ◽  
Privacy Preserving ◽  
D2d Communication ◽  
Group Data ◽  
Big Data Privacy ◽  
Daunting Challenge

As a novel and promising technology for 5G networks, device-to-device (D2D) communication has garnered a significant amount of research interest because of the advantages of rapid sharing and high accuracy on deliveries as well as its variety of applications and services. Big data technology offers unprecedented opportunities and poses a daunting challenge to D2D communication and sharing, where the data often contain private information concerning users or organizations and thus are at risk of being leaked. Privacy preservation is necessary for D2D services but has not been extensively studied. In this paper, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. Firstly, we provide a framework for the D2D big data sharing and analyze the threat model. Then, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. In our privacy-preserving framework, we adopt (a, k)-anonymity as privacy-preserving model for D2D big data and use the distributed MapReduce to classify and group data for massive datasets. The results of experiments and theoretical analysis show that our privacy-preserving algorithm deployed on MapReduce is effective for D2D big data privacy protection with less information loss and computing time.

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