Big-tech corporations like Google, Meta, Microsoft etc. extensively utilize customization to collect and analyze user data, a practice integral to their business models. Google leverages user data to personalize services across its platforms, notably in its search engine and YouTube, to enhance user experience and bolster its targeted advertising strategies. Similarly, Meta uses algorithmic content curation on Facebook and Instagram, tailoring user feeds to individual preferences and behaviors, thereby generating detailed user profiles for marketing purposes. Microsoft's approach, particularly with Office 365 and LinkedIn, focuses on productivity enhancements while also gathering user data for feature refinement and targeted advertising. These practices, I argue, while improving user engagement, raise significant privacy concerns. The extensive data collection often occurs without full transparency or user consent, leading to debates about ethical implications, digital surveillance, and societal impacts. In response, there is a growing demand for stricter data governance and privacy regulations, as seen in initiatives like the GDPR and CCPA, aiming to balance the benefits of personalization with the rights and privacy of users.

The small world network idea recently has been introduced in order to optimize the performance of the Networks-on-chip. Based on this method the architecture will be neither fully customized nor completely regular. Results have shown that by using the longrange links which optimized the network power and performance, the area consumption will exceed. We can derive from this that an acceptable bound on the area consumption should be considered. Based on the restriction of a designer, in this paper we want to present a methodology that will automatically optimize an architecture while at the same time considering the area consumption.