Search arXivSearch

arXiv · 2602.18616

Evaluating PDPL Compliance in E-Commerce Websites: Insights and Lessons Learned from Human and LLM Analyses

Abstract

In 2024, Saudi Arabia's Personal Data Protection Law (PDPL) came into force. However, little work has been done to assess its implementation. In this paper, we analyzed 100 e-commerce websites operating in Saudi Arabia against the PDPL, examining the presence of a privacy policy and, if present, the policy's declarations of four items pertaining to personal data rights and practices: 1) personal data retention period, 2) the right to request the destruction of personal data, 3) the right to request a copy of personal data, and 4) a mechanism for filing complaints. Our results show that, despite national awareness and support efforts, a significant fraction of e-commerce websites in our dataset are not fully compliant: only 31% of websites in our dataset declared all four examined items in their privacy policies. Even when privacy policies included such declarations, a considerable fraction of them failed to cover required fine-grained details. Second, the majority of top-ranked e-commerce websites in our dataset (based on search results order) and those hosted on local e-commerce hosting platforms exhibited considerably higher non-compliance rates than mid- to low-ranked websites and those not hosted on local e-commerce platforms. Third, we assessed the use of Large Language Models (LLMs) as an automated tool for privacy policy analysis to measure compliance with the PDPL. We highlight the potential of LLMs and suggest considerations to improve LLM-based automated analysis for privacy policies. Our results provide a step forward in understanding the implementation barriers to data protection laws, especially in non-Western contexts. We provide recommendations for policymakers, regulators, website owners, and developers seeking to improve data protection practices and automate compliance monitoring.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Eman Alashwali, Abeer Alhuzali. 2026-03-31. Evaluating PDPL Compliance in E-Commerce Websites: Insights and Lessons Learned from Human and LLM Analyses. https://arxiv.org/abs/2602.18616

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Learning Password Best Practices Through In-Task Instruction

Users often make security- and privacy-relevant decisions without a clear understanding of the rules that govern safe behavior. We introduce pedagogical friction, a design approach that inserts brief, instructional interactions at the moment of action. We evaluate this approach in the context of password creation, a familiar task with clear quality criteria. We conducted a randomized study with 128 participants across four interface conditions that varied the depth and interactivity of guidance. We assessed three outcomes: (1) rule compliance in a subsequent password task without guidance, (2) accuracy on survey questions tied to password rules, and (3) behavior-knowledge alignment, which captures whether participants who correctly followed a rule also recognized it on the survey. Across the guided conditions, participants corrected most rule violations in the follow-up task and showed high behavior-knowledge alignment. Survey results suggested clearer advantages for some rule types, especially symbol related questions. These results position pedagogical friction as a lightweight intervention for security- and privacy-critical interfaces.

cs.HC

Leveling the Playing Field: Temporal Video Segmentation for Individuals with ADHD in Computing Education

Individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) often face significant barriers in computing education. In asynchronous learning environments, instructional videos can impose high extraneous cognitive load, often relying on assumptions about sustained attention and working memory that do not align with ADHD neurocognitive profiles. In this work, we evaluate a post-hoc video processing intervention that segments instructional content into single-instruction chunks followed by fixed-length pauses to reduce cognitive load. In a within-participants controlled study with 17 individuals with ADHD and 10 without, we find that the intervention has an equalizing effect. Although it improved performance for all participants, gains were larger for those with ADHD, reducing their errors and hesitations to levels comparable to those of participants without ADHD under the same intervention. These results align with the goals of Universal Design for Learning (UDL), by showing that cognitively-aligned, post-hoc instructional video modifications can reduce performance disparities across diverse neurocognitive profiles.

cs.HC

Human Driver Temperament and the Safety Impact of a C-V2X Denial-of-Service Flooding Attack in Mixed-Autonomy Traffic

Cooperative and connected automated vehicles (CAVs) rely on Signal Phase and Timing (SPaT) messages to cross signalized intersections; a denial-of-service (DoS) flood that blocks SPaT forces CAVs into a fail-safe mode. Because human-driven vehicles share the intersection, the safety consequence depends not only on the attack and the CAV fail-safe policy, but on how the surrounding human drivers behave. We investigate this human-factors dimension with a coupled OMNeT++/INET (5G NR-V2X) and SUMO microsimulation of a signalized corridor, sweeping CAV market penetration (10-90%), four calibrated driver temperaments (cautious to aggressive) and two standards-based fail-safe policies, a minimal-risk maneuver (MRM) and an adaptive cruise control (ACC) keep-driving fallback, with each attack arm differenced against its policy-matched no-attack baseline. Temperament's effect on the attack is specific and modest rather than a blanket amplification. Aggressive surroundings worsen one metric, the hard-braking a keep-driving fail-safe forces on nearby drivers (p = 0.03), rising from near zero to +8 episodes/1000 veh-s. They appear to dampen rear-end conflicts, but only because the flood clears the queues aggressive drivers build, so the gain is in flow, not safety. On the attack's primary signatures, CAV red-light running and crossing conflicts, temperament has no detectable effect. It instead dominates baseline risk, producing a 13- to 18-fold cautious-to-aggressive gradient far larger than the attack itself, which acts through a channel already congested by CAV adoption. Human driver populations determine how dangerous the intersection is but do not systematically amplify this attack, so fail-safe design cannot assume a cautious test population bounds the risk.

cs.HC