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- Ahmadi Mehri, Vida, et al.
(författare)
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Privacy and trust in cloud-based marketplaces for AI and data resources
- 2017
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Ingår i: IFIP Advances in Information and Communication Technology. - Cham : Springer New York LLC. - 9783319591704 ; , s. 223-225
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Konferensbidrag (refereegranskat)abstract
- The processing of the huge amounts of information from the Internet of Things (IoT) has become challenging. Artificial Intelligence (AI) techniques have been developed to handle this task efficiently. However, they require annotated data sets for training, while manual preprocessing of the data sets is costly. The H2020 project “Bonseyes” has suggested a “Market Place for AI”, where the stakeholders can engage trustfully in business around AI resources and data sets. The MP permits trading of resources that have high privacy requirements (e.g. data sets containing patient medical information) as well as ones with low requirements (e.g. fuel consumption of cars) for the sake of its generality. In this abstract we review trust and privacy definitions and provide a first requirement analysis for them with regards to Cloud-based Market Places (CMPs). The comparison of definitions and requirements allows for the identification of the research gap that will be addressed by the main authors PhD project. © IFIP International Federation for Information Processing 2017.
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| 2. |
- Fritsch, Lothar, 1970-
(författare)
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Partial commitment – "Try before you buy" and "Buyer’s remorse" for personal data in Big Data & Machine learning
- 2017
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Ingår i: Trust Management XI. - Cham, Switzerland : Springer. - 9783319591704 - 9783319591711 ; , s. 3-11
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Konferensbidrag (refereegranskat)abstract
- The concept of partialcommitment is discussed in the context of personal privacy management in datascience. Uncommitted, promiscuous or partially committed user’s data may eitherhave a negative impact on model or data quality, or it may impose higherprivacy compliance cost on data service providers. Many Big Data (BD) andMachine Learning (ML) scenarios involve the collection and processing of largevolumes of person-related data. Data is gathered about many individuals as wellas about many parameters in individuals. ML and BD both spend considerable resourceson model building, learning, and data handling. It is therefore important toany 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. Additionalcost is imposed by data protection regulation with transparency, revocation andcorrection rights for data subjects. Data subjects may, for several reasons, only partially accept a privacypolicy, and chose to opt out, request data deletion or revoke their consent fordata processing. This article discusses the concept of partial commitment andits possible applications from both the data subject and the data controllerperspective in Big Data and Machine Learning.
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