Your browsing behavior for a Big Mac: Economics of Personal Information Online (1112.6098v1)

Abstract: Most online services (Google, Facebook etc.) operate by providing a service to users for free, and in return they collect and monetize personal information (PI) of the users. This operational model is inherently economic, as the "good" being traded and monetized is PI. This model is coming under increased scrutiny as online services are moving to capture more PI of users, raising serious privacy concerns. However, little is known on how users valuate different types of PI while being online, as well as the perceptions of users with regards to exploitation of their PI by online service providers. In this paper, we study how users valuate different types of PI while being online, while capturing the context by relying on Experience Sampling. We were able to extract the monetary value that 168 participants put on different pieces of PI. We find that users value their PI related to their offline identities more (3 times) than their browsing behavior. Users also value information pertaining to financial transactions and social network interactions more than activities like search and shopping. We also found that while users are overwhelmingly in favor of exchanging their PI in return for improved online services, they are uncomfortable if these same providers monetize their PI.

• The paper finds that users assign significantly higher monetary values to offline personal information compared to online browsing data.
• It employs a reverse second-price auction and experience sampling method to elicit truthful valuations across various PI categories.
• Findings highlight users' discomfort with data monetization practices, emphasizing a need for greater transparency and redesigned privacy policies.

Economics of Personal Information Online: An Analysis of User Valuation

The paper "Your browsing behavior for a Big Mac: Economics of Personal Information Online" investigates the economic valuation that users place on their personal information (PI) when interacting online. The paper explores two primary questions: the monetary value users assign to different types of PI in varied online contexts and their perceptions regarding the monetization of this information by service providers.

Methodology and Experimental Design

The researchers employed an Experience Sampling Method, refined to capture the context in which users value their PI. A browser plugin was designed to observe participants' web activity and prompt them with surveys and an auction game to assess their willingness to sell specific types of PI. The paper involved 168 participants who were observed over six weeks. They were asked to bid on their PI across several categories, including their offline identity, web behavior, and interactions on specific online services like social media and financial platforms. The auction design chosen was a reverse second-price auction, encouraging truthful bidding by allowing the highest bidder to purchase the PI at the second-highest bid.

Key Findings

1. Valuation Discrepancy Between Offline and Online PI: The paper reveals that participants place a higher monetary value on their offline PI, such as age, gender, and financial status, compared to their online browsing data. This suggests a perceived increased risk or importance of offline PI.
2. Category-Specific PI Valuation: Participants valued PI related to financial transactions and social media interactions higher than information pertaining to general browsing or search history. This indicates a perceived higher importance or vulnerability associated with financial and personal social data.
3. Monetization Discomfort: Despite an awareness of PI exploitation for monetary gain, participants expressed discomfort with the monetization of their PI by online service providers. However, they were more accepting of their PI being used for service enhancement.
4. Personal Data Bulk Sale Indifference: Interestingly, participants showed no significant difference in valuation when asked to sell individual pieces of PI compared to bulk data packages, suggesting a potential gap in understanding the cumulative value of personal data.

Theoretical and Practical Implications

• Marketization of PI: The findings support the potential development of a marketplace for PI where users can trade their own data consciously and transparently. This could potentially reduce privacy concerns by offering clearer compensation models for PI.
• Transparency in Data Monetization: The discomfort with PI monetization highlights the necessity for clearer communication from service providers regarding how user data is being used and monetized. Greater transparency may align user expectations with service provider practices and reduce privacy concerns.
• Redesign of Privacy Policies: The insights suggest that companies should reconsider the design of their privacy notices, ensuring that the transactional nature of data use is presented upfront, possibly helping users better understand the value exchange inherent in "free" online services.

Future Directions

Future research should explore how these findings can be integrated into privacy-preserving technologies and regulations to enhance user trust and data security. Additionally, more cross-cultural studies could help determine if these valuation patterns hold globally or are subject to regional variations.

In conclusion, the paper provides a quantitative assessment of how users value different types of their PI and highlights significant insights into their perceptions of data monetization. These findings could guide future policy-making and commercial strategies in the digital economy by accommodating user preferences regarding their personal data.