Privacy-respecting collaborative data analysis

数字经济由（大）数据驱动。几乎所有利益相关者都利用无处不在的数据收集来获取经济利益。例如，银行可以通过分析客户的手机数据为他们提供更好的服务。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，法律和社会可接受性有必要最大限度地保护个人隐私，防止不必要的推断，同时仍然允许为社会或经济利益使用数据。由于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保管数据，因此这一挑战更加严峻。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过程中保护隐私，例如使用同态或功能加密和安全的多方计算。在这个建议中，我们超越了单一技术的优化，而是在适当的-通常结合-使用这两种技术在保护隐私的数据分析中的重要应用。我们评估了所提出的解决方案的安全性和效率，并与现有的方法进行了比较，这些方法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.