My Data, Your Data, Our Data: Managing Privacy Preferences in Multiple Subjects Personal Data

2014 ◽  
pp. 154-171 ◽  
Author(s):  
Stefania Gnesi ◽  
Ilaria Matteucci ◽  
Corrado Moiso ◽  
Paolo Mori ◽  
Marinella Petrocchi ◽  
...  
Keyword(s):  
Personal Data ◽  
Privacy Preferences

scholarly journals Unpacking Privacy: Willingness to pay to protect personal data

2019 ◽  
Author(s):  
Anya Skatova ◽  
Rebecca Louise McDonald ◽  
Sinong Ma ◽  
Carsten Maple
Keyword(s):  
Business Models ◽  
Personal Data ◽  
Digital Economy ◽  
Privacy Preferences ◽  
Individual Privacy ◽  
Free Service ◽  
Different Types ◽  
The Government ◽  
Evaluation Techniques ◽  
Future Business

Data is key for the digital economy, underpinning business models and service provision, and a lot of these valuable datasets are personal in nature. Information about individual behaviour is collected regularly by organisations. This information has value to businesses, the government and third parties. It is not clear what value this personal data has to consumers themselves. Much of the digital economy is predicated on people sharing personal data, however if individuals value their privacy, they may choose to withhold this data unless the perceived benefits of sharing outweigh the perceived value of keeping the data private. Further, they might be willing to pay for an otherwise free service if paying allowed them to avoid sharing personal data. We used five evaluation techniques to study preferences for protecting personal data online and found that consumers assign a positive value to keeping a variety of types of personal data private. We show that participants are prepared to pay different amounts to protect different types of data, suggesting there is no simple function to assign monetary value that can be identified for individual privacy in the digital economy. The majority of participants displayed remarkable consistency in their rankings of the importance of different types of data, a finding that indicates the existence of stable individual privacy preferences in protecting personal data. We discuss our findings in the context of research on the value of privacy and privacy preferences, and in terms of implications for future business models and consumer protection.

scholarly journals The posthumous privacy paradox: Privacy preferences and behavior regarding digital remains

2020 ◽  
pp. 146144482097495
Author(s):  
Tal Morse ◽  
Michael Birnhack
Keyword(s):  
Representative Sample ◽  
Personal Data ◽  
Stated Preferences ◽  
Actual Behavior ◽  
Privacy Preferences ◽  
Internet Users ◽  
Privacy Paradox ◽  
And Behavior

Scholars have observed a gap between users’ stated preferences to protect their privacy and their actual behavior. This is the privacy paradox. This article queries the persistence of the privacy paradox after death. A survey of a representative sample of Israeli Internet users inquired of perceptions, preferences, and actions taken by users regarding their digital remains. The analysis yielded three distinct groups: (1) users interested in preserving privacy posthumously but do not act accordingly; for these users, the privacy paradox persists posthumously; (2) users who match their behavior to their preferences; for these users, the privacy paradox is resolved; and (3) users interested in sharing their personal data posthumously but do not make the appropriate provisions. This scenario is the inverted privacy paradox. This new category has yet to be addressed in the literature. We present some explanations for the persistence of the posthumous privacy paradox and for the inverted privacy paradox.

Direct Consumer Influence—The Missing Strategy to Integrate Data Privacy Preferences into the Market

2020 ◽  
Author(s):  
Adrian Kuenzler
Keyword(s):  
Data Privacy ◽  
Business Models ◽  
Data Gathering ◽  
Personal Data ◽  
Competition Law ◽  
Consumer Sovereignty ◽  
Digital Platforms ◽  
Privacy Preferences ◽  
Novel Approach ◽  
Quality Dimensions

Abstract In view of a growing number of competition law investigations into the gathering and use of personal data by digital platforms, this article discusses the extent to which consumer sovereignty can be given greater weight in concentrated marketplaces where firms employ multi-sided business models and compete along quality dimensions such as privacy rather than price. The article explores the concept of direct consumer influence as a novel approach vis-à-vis switching or choosing differently in the public enforcement of competition law. Direct consumer influence constitutes a distinct avenue for embedding consumers’ choices into the market when consumers have few possibilities to act and holds the potential to shape digital markets in unanticipated ways. Using the example of the German Federal Cartel Office’s investigation into Facebook’s data-gathering practices, the article illustrates how direct consumer influence may clarify the relationship between data protection, consumer rights, and competition law.

scholarly journals Privacy Preference Signals: Past, Present and Future

2021 ◽  
Vol 2021 (4) ◽  
pp. 249-269
Author(s):  
Maximilian Hils ◽  
Daniel W. Woods ◽  
Rainer Böhme
Keyword(s):  
High Frequency ◽  
Personal Data ◽  
Coordination Problem ◽  
Early Adopters ◽  
Privacy Preferences ◽  
Digital Representations ◽  
Privacy Preference ◽  
Privacy Laws ◽  
History Of ◽  
To Receive

Abstract Privacy preference signals are digital representations of how users want their personal data to be processed. Such signals must be adopted by both the sender (users) and intended recipients (data processors). Adoption represents a coordination problem that remains unsolved despite efforts dating back to the 1990s. Browsers implemented standards like the Platform for Privacy Preferences (P3P) and Do Not Track (DNT), but vendors profiting from personal data faced few incentives to receive and respect the expressed wishes of data subjects. In the wake of recent privacy laws, a coalition of AdTech firms published the Transparency and Consent Framework (TCF), which defines an optin consent signal. This paper integrates post-GDPR developments into the wider history of privacy preference signals. Our main contribution is a high-frequency longitudinal study describing how TCF signal gained dominance as of February 2021. We explore which factors correlate with adoption at the website level. Both the number of third parties on a website and the presence of Google Ads are associated with higher adoption of TCF. Further, we show that vendors acted as early adopters of TCF 2.0 and provide two case-studies describing how Consent Management Providers shifted existing customers to TCF 2.0. We sketch ways forward for a pro-privacy signal.

Automated Privacy Preferences for Smart Home Data Sharing Using Personal Data Stores

2021 ◽  
pp. 2-12
Author(s):  
Yashothara Shanmugarasa ◽  
Hye-Young Paik ◽  
Salil Kanhere ◽  
Liming Zhu
Keyword(s):  
Data Sharing ◽  
Smart Home ◽  
Personal Data ◽  
Privacy Preferences

SARI : A User-Oriented Data Bank System for Medical Applications

1976 ◽  
Vol 15 (02) ◽  
pp. 69-74
Author(s):  
M. Goldberg ◽  
B. Doyon
Keyword(s):  
Data Base ◽  
Personal Data ◽  
Data Bank ◽  
Medical Applications ◽  
Basic Principles ◽  
Base Management ◽  
General Data ◽  
Privacy Problem ◽  
Bank System ◽  
Made In

This paper describes a general data base management package, devoted to medical applications. SARI is a user-oriented system, able to take into account applications very different by their nature, structure, size, operating procedures and general objectives, without any specific programming. It can be used in conversational mode by users with no previous knowledge of computers, such as physicians or medical clerks.As medical data are often personal data, the privacy problem is emphasized and a satisfactory solution implemented in SARI.The basic principles of the data base and program organization are described ; specific efforts have been made in order to increase compactness and to make maintenance easy.Several medical applications are now operational with SARI. The next steps will mainly consist in the implementation of highly sophisticated functions.

Measurements method of the audio recordings acoustic quality indicator prepared for registration and processing in the Unified Biometric System

2019 ◽  
pp. 40-46 ◽  
Author(s):  
V.V. Savchenko ◽  
A.V. Savchenko
Keyword(s):  
Quality Control ◽  
Small Sample Size ◽  
Personal Data ◽  
Digital Processing ◽  
Small Sample ◽  
Practical Implementation ◽  
Acoustic Measurements ◽  
Pitch Frequency ◽  
Wide Range ◽  
Automated Quality Control

We consider the task of automated quality control of sound recordings containing voice samples of individuals. It is shown that in this task the most acute is the small sample size. In order to overcome this problem, we propose the novel method of acoustic measurements based on relative stability of the pitch frequency within a voice sample of short duration. An example of its practical implementation using aninter-periodic accumulation of a speech signal is considered. An experimental study with specially developed software provides statistical estimates of the effectiveness of the proposed method in noisy environments. It is shown that this method rejects the audio recording as unsuitable for a voice biometric identification with a probability of 0,95 or more for a signal to noise ratio below 15 dB. The obtained results are intended for use in the development of new and modifying existing systems of collecting and automated quality control of biometric personal data. The article is intended for a wide range of specialists in the field of acoustic measurements and digital processing of speech signals, as well as for practitioners who organize the work of authorized organizations in preparing for registration samples of biometric personal data.

Conflict Detection Techniques for Preserving Privacy in Social Media

2018 ◽  
pp. 78-85
Author(s):  
Fulpagare Priya K. ◽  
Nitin N. Patil
Keyword(s):  
Social Media ◽  
Privacy Management ◽  
Crucial Importance ◽  
Network Integration ◽  
Critical Problem ◽  
Detection Techniques ◽  
Privacy Preferences ◽  
Content Sharing ◽  
Privacy Settings ◽  
Relationship Network

Social Network is an emerging e-service for Content Sharing Sites (CSS). It is an emerging service which provides reliable communication. Some users over CSS affect user’s privacy on their personal contents, where some users keep on sending annoying comments and messages by taking advantage of the user’s inherent trust in their relationship network. Integration of multiple user’s privacy preferences is very difficult task, because privacy preferences may create conflict. The techniques to resolve conflicts are essentially required. Moreover, these methods need to consider how users would actually reach an agreement about a solution to the conflict in order to offer solutions acceptable by all of the concerned users. The first mechanism to resolve conflicts for multi-party privacy management in social media that is able to adapt to different situations by displaying the enterprises that users make to reach a result to the conflicts. Billions of items that are uploaded to social media are co-owned by multiple users. Only the user that uploads the item is allowed to set its privacy settings (i.e. who can access the item). This is a critical problem as users’ privacy preferences for co-owned items can conflict. Multi-party privacy management is therefore of crucial importance for users to appropriately reserve their privacy in social media.

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