Does Personalised Advertisement Based On Your Searches Violate Your Privacy? Aayush Mudgal Explains

Imagine a scenario where you search for a new pair of shoes online, and suddenly, shoe advertisements spring to life across the diverse websites and apps you use. You must have come across such an experience. This happens due to the seamless exchange of data between advertisers and advertising platforms, coupled with the intricacies of complex machine learning models, bringing forth personalized ads in an effective manner. 

“Advertisers have honed the art of using tracking technologies, from cookies to device fingerprinting and cross-device tracking, to curate digital dossiers of users' interests and preferences. By doing so, they can identify users across various sites and the result is a flood of personalized ads that appear seamlessly across diverse websites and apps, shaping a vibrant digital ecosystem that benefits developers and publishers alike by utilizing complex machine learning models in the process”, said Mudgal, who has extensive experience in building deep learning models for ads recommendation at esteemed companies. 

Numerous studies have shown the value created by personalized ads, contributing billions of dollars to publisher revenue. Personalization empowers advertisers to offer relevant products and services, leading to more engaging ad experiences and increased customer satisfaction. Nonetheless, this evolution in ads tracking has ignited heated debates on the ethical boundaries of personalized advertising, necessitating the evolution of privacy regulations to keep pace with these advancements. Users are now being granted more control over their data, making it imperative for them to comprehend the mechanics of ads tracking to safeguard their online privacy. Anticipated actions by other providers to follow suit imply that users will gain more privacy control, enabling them to tailor their data-sharing preferences to suit their needs. 

As the digital advertising realm undergoes transformative shifts and moves away from individual third-party data, privacy-enhancing techniques like differential privacy, federated learning, and homomorphic encryption are gaining prominence. These techniques, passionately advocated by experts like Aayush, offer innovative solutions to balance personalization with user privacy. Privacy-enhancing techniques have long been prominent in domains like finance and medicine, where the protection of sensitive data is of utmost importance. However, in recent times, these techniques have gained even more prominence due to the powerful push for privacy in the ads domain. Aayush helped us understand prominent techniques in this area.

Differential privacy, explains Aayush Mudgal, emerges as a formidable defence against intrusive data analysis. By strategically injecting precisely calibrated noise into data prior to processing, organizations can effectively shield individual privacy without compromising the overall data utility. The ingenious concept of differential privacy renders it nearly insurmountable to link specific individuals to their data, even under malicious attempts at identification. Advertisers are also increasingly adopting user-initiated data access and deletion mechanisms, granting users the ability to access and delete their data upon request. This commitment to respecting user privacy demonstrates a shift towards responsible data handling.

Federated learning is a decentralized approach that trains models on user devices, sending only aggregated updates to a central server. With this technique, advertisers can personalize ads based on user behaviour without transferring raw user data, ensuring user privacy remains intact, explained Aayush

In the digital age, personalized ads have become an integral part of the advertising ecosystem, offering significant value to both advertisers and users. However, ethical considerations and evolving privacy regulations are driving the industry to adopt privacy-enhancing techniques to safeguard user privacy. “The responsible and transparent use of data will shape the future of personalized advertising, ensuring a win-win situation for all stakeholders involved”, said Mudgal.