DeAD

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models’ accuracy and ignore issues related to security and the users’ privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users’ private information.

To bridge this gap, DeAD is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics.

DeAD also offers benefits to the user for using the recommender system by incentivising them for every choice/preference/click.

The dApp uses the scalability of Polygon and decentralized storage of IPFS to provide a decentralized, censorship-resistant, and user consent-based ad recommendation system. It has three key participants Creator, Supplier, and User. The creator uploads the content it wants to show as ad, its associated tags, and the incentive( for supplier and user) which gets stored onchain. The supplier then gets the ad for a specific tag and runs the recommendation algorithm to rank the ads based on their correlation score. The user then views the ad and. if they click on any ad, the user gets incentivized for watching the ad and the supplier gets incentivized for recommending the ad to the user. The contracts are deployed on Polygon for seamless interaction and IPFS to host the ads supplied by the supplier.