A blockchain‐based privacy‐preserving advertising attribution architecture: Requirements, design, and a prototype implementation

Abstract

In the era of digital marketing, advertisements have become an indispensable part. One of the central challenges is advertising attribution which explains the amount of contribution every publisher has with the conversions. However, through observation, we have found that current advertising platforms attribution, advertisers attribution, or third-party platforms attribution all have the problems of trust, data leakage, and data forgery. To fill the gap, our work's main contribution is combining blockchain with advertising attribution to propose an architecture for improving the privacy-preserving degree and amount. In the proposed architecture, publishers, and advertisers can store real-time data on a blockchain. The attribution results are credible because blockchain is decentralized, tamper-proof, and traceable. We combine privacy set intersection and zero-knowledge proof technology to increase the privacy of flowing data. In addition, we describe a preliminary prototype in which publishers, advertisers, and advertising platforms can get the corresponding attribution details. To show its effectiveness, we analyze it from different perspectives, including communication cost, attribution accuracy, and time cost. The results show that our communication cost has significantly reduced compared to the recent studies.

Blockchain Framework for Digital Learning and Information and Communications Technology

“…Due to the centralized approach to cross-border payments, this is associated with several security risks, even though the agent's tier structure may result in higher fees for cross-border payments than the addition of domestic ones. The blockchain can perform multi-party identity authentication, effectively protecting the private information of users from being leaked, And by the smart contract operation of every node in the blockchain, it can solve the "trust crisis" existing in the traditional Internet model [29]. Big data analytics is an important foundation for building new business models and achieving customer value and can be used to optimize business operating strategy and marketing methods and improve business profitability.…”

Privacy‐preserving task offloading in mobile edge computing: A deep reinforcement learning approach

“…The data collected by users may face privacy issues, and the privacy should be protected. Liu et al 12 designed a block chain based framework to protect data privacy. To deal with data privacy, our paper proposes a local differential privacy based data privacy protection algorithm, which is different from other works in its approach to privacy protection.…”

“…The energy cost c i could be influenced by U (i,t) which stands for offloading data and T (i,t) which stands for data training locally. The total energy e (i,t) could be calculated with Equation (12). 𝛼 is variable factor that could be changed with specified environment.…”

“…However, since edge servers are not completely trusted, there is still a risk of privacy leakage when using MEC for ML model training. This risk can be further reduced through techniques such as differential privacy (DP), 11 block chain 12 and homomorphic encryption. 13 Currently, most researches focus on improving the efficiency and accuracy of ML model training, with relatively little consideration given to privacy protection.…”