Partial Commitment – “Try Before You Buy” and “Buyer’s Remorse” for Personal Data in Big Data & Machine Learning

Abstract : The concept of partial commitment is discussed in the context of personal privacy management in data science. Uncommitted, promiscuous or partially committed user’s data may either have a negative impact on model or data quality, or it may impose higher privacy compliance cost on data service providers. Many Big Data (BD) and Machine Learning (ML) scenarios involve the collection and processing of large volumes of person-related data. Data is gathered about many individuals as well as about many parameters in individuals. ML and BD both spend considerable resources on model building, learning, and data handling. It is therefore important to any BD/ML system that the input data trained and processed is of high quality, represents the use case, and is legally processes in the system. Additional cost is imposed by data protection regulation with transparency, revocation and correction rights for data subjects. Data subjects may, for several reasons, only partially accept a privacy policy, and chose to opt out, request data deletion or revoke their consent for data processing. This article discusses the concept of partial commitment and its possible applications from both the data subject and the data controller perspective in Big Data and Machine Learning.
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Lothar Fritsch. Partial Commitment – “Try Before You Buy” and “Buyer’s Remorse” for Personal Data in Big Data & Machine Learning. 11th IFIP International Conference on Trust Management (TM), Jun 2017, Gothenburg, Sweden. pp.3-11, ⟨10.1007/978-3-319-59171-1_1⟩. ⟨hal-01651157⟩

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