Privacy-respecting collaborative data analysis

数字经济是由(大数据)驱动的。几乎所有利益攸关方无处不在的数据收集被用来为他们的经济利益服务。例如，银行可以通过分析他们的手机数据为客户提供更好的服务。然而，这些数据不仅可能被用于社会利益，还可能被用于不受欢迎甚至被禁止的用途，例如侵犯个人隐私。因此，根据法律和为社会所接受，有必要最大限度地保护个人隐私，防止不必要的推断，同时仍然允许将数据用于社会或经济利益。这一挑战加剧，因为数据可能分布在几个实体之间，每个实体都被委托为特定目的提供数据。 差异隐私可用于保护个人免受数据分析结果的推断。密码学可以在数据存储和处理期间保护隐私，例如使用同态或函数加密和安全多方计算。在本提案中，我们不仅着眼于单一技术的优化，而且着眼于这两种技术在隐私保护数据分析中的重要应用的适当使用--通常是组合使用。我们评估了建议的解决方案与现有方法的安全性和效率，旨在达到金融行业数据保护可接受的隐私水平。

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.