Privacy-respecting collaborative data analysis

点击翻译按钮获取中文摘要

The digital economy is driven by (big) data. The ubiquitous collection of data by almost all stakeholders is exploited for their economic benefit. For example, banks may provide better services to their customers by the analysis of their cell phone data. However that data may not only be used for social benefit, but also unwanted or even prohibited uses, e.g. violating one's privacy. Hence, it is necessary by law and for social acceptability to protect the privacy of individuals to the utmost possible extent and prevent unwanted inferences while still enabling the use of the data for the social or economic benefit. This challenge is exacerbated, since the data may be distributed among several entities each of whom has been entrusted with the data for a specific purpose. Differential privacy can be used to protect an individual against inferences from the result of a data analysis. Cryptography can protect privacy during data storage and processing, e.g. using homomorphic or functional encryption and secure multi-party computation. In this proposal we look beyond the optimization of a single technology, but at the appropriate - often combined - use of those two technologies for important application in privacy-preserving data analysis. We evaluate the security and efficiency of the proposed solutions in comparison to existing approaches aiming at a privacy level acceptable for data protection in the financial industry.