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The pervasiveness of Internet of Things results in vast volumes of personaldata generated by smart devices of users (data producers) such as smart phones,wearables and other embedded sensors. It is a common requirement, especiallyfor Big Data analytics systems, to transfer these large in scale anddistributed data to centralized computational systems for analysis.Nevertheless, third parties that run and manage these systems (data consumers)do not always guarantee users' privacy. Their primary interest is to improveutility that is usually a metric related to the performance, costs and thequality of service. There are several techniques that mask user-generated datato ensure privacy, e.g. differential privacy. Setting up a process for maskingdata, referred to in this paper as a `privacy setting', decreases on the onehand the utility of data analytics, while, on the other hand, increasesprivacy. This paper studies parameterizations of privacy-settings that regulatethe trade-off between maximum utility, minimum privacy and minimum utility,maximum privacy, where utility refers to the accuracy in the approximations ofaggregation functions. Privacy settings can be universally applied assystem-wide parameterizations and policies (homogeneous data sharing).Nonetheless they can also be applied autonomously by each user or decided underthe influence of (monetary) incentives (heterogeneous data sharing). Thislatter diversity in data sharing by informational self-determination plays akey role on the privacy-utility trajectories as shown in this paper boththeoretically and empirically. A generic and novel computational framework isintroduced for measuring privacy-utility trade-offs and their optimization. Theframework computes a broad spectrum of such trade-offs that formprivacy-utility trajectories under homogeneous and heterogeneous data sharing.

Optimization of Privacy-Utility Trade-offs under Informational Self-determination