P Computation: A theoretical and practical implementation; Data ownership, governance, and privacy: China's TikTok and the United States' Facebook

VanDerzee, Robert, School of Engineering and Applied Science, University of Virginia
Ku, Tsai-Hsuan, EN-Engineering and Society, University of Virginia
Brunelle, Nathan, EN-Comp Science Dept, University of Virginia

Our case analysis of data ownership, governance, and privacy entails investigating the in-depth historical developments of two technology companies, TikTok and Facebook, within the United States. Exponentially increasing information processing capabilities have allotted social networking companies significantly more power with regards to obtaining, analyzing, and marketing user data to advertisers, governments, and amongst themselves; as well as adapting societal norms, through institutions or otherwise, in favor of their bottom line. For United States based companies, it has become characteristic for institutional assertions about user privacy to become proportionally lax with these rapid advancements; colloquially known as the "you are the product" phenomenon. In turn, many of these highly dynamic policy agreements adapt in unfavorable ways to the user long after the company’s products have widespread market and cultural adoption; often immediately following unrest from either poor public perception of or legislative penalty from their decisions.

Facebook is the archetypal example of such a company since their inception around 2004. For foreign companies operating within the United States, this expectation can be vastly different; especially with regards to TikTok which originates from China. In fact, TikTok’s analogous privacy policy is highly static and rarely, if at all, changed as a result of public or legislative pressures. Instead, TikTok is afflicted with preemptive restraint due to various sociopolitical reasons; widely regarded to be a result of political adversary between the United States and China. Our analysis of these policies and adaptations from both a cultural and economic perspective attempts to fully assess the dynamic and compartmentalize events into three distinct but functionally codependent categories: data ownership, governance, and privacy.

Data Ownership refers to the general establishment of legal rights over the control of digital data, including the rights of acquisition, use, and distribution. Specifically, we view data ownership in the consumer space of Facebook and TikTok which are legislatively upheld through terms of service and privacy policies in the United States. As corollary, we analyze historical breaches of these agreements and the resulting reparations and policy adaptations as evidence and context to the present state of data ownership for those connected to these systems.

Data Governance refers to the examination of data relations with respect to the long term technical development of these companies, both internally in company vision and externally through legislative bodies. Internal systems can source the discussion of relations in order to better connect governance and the other two areas of interest. Similarly, it is fundamentally related to the case analyses involving data privacy negligence.

Data Privacy refers to the dynamic between how data is used and how the public perceives how data is used throughout time. These perceptions contain ethical statements about the state of an event, and arise context for both Data Governance and Data Ownership evolution. As these large technical social networks press these societal expectations, public awareness and action proportionally rise, or conversely.

Note that the technical subject of the STS thesis and the technical topic of the Department of Computer Science are only loosely related. The technical topic assesses an alternative number representation system within the theoretical and practical space, which in part, investigates the effect of P Computation on the preexisting RSA security algorithm which enforces privatization of data through the network between agents and other systems by leveraging the complexity of prime number factorization.

BS (Bachelor of Science)

School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Nathan Brunelle
STS Advisor: Tsai-Hsuan Ku
Technical Team Members: Robert VanDerzee

Issued Date: