  The pervasiveness of Internet of Things results in vast volumes of personal
data generated by smart devices of users (data producers) such as smart phones,
wearables and other embedded sensors. It is a common requirement, especially
for Big Data analytics systems, to transfer these large in scale and
distributed 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 improve
utility that is usually a metric related to the performance, costs and the
quality of service. There are several techniques that mask user-generated data
to ensure privacy, e.g. differential privacy. Setting up a process for masking
data, referred to in this paper as a `privacy setting', decreases on the one
hand the utility of data analytics, while, on the other hand, increases
privacy. This paper studies parameterizations of privacy-settings that regulate
the trade-off between maximum utility, minimum privacy and minimum utility,
maximum privacy, where utility refers to the accuracy in the approximations of
aggregation functions. Privacy settings can be universally applied as
system-wide parameterizations and policies (homogeneous data sharing).
Nonetheless they can also be applied autonomously by each user or decided under
the influence of (monetary) incentives (heterogeneous data sharing). This
latter diversity in data sharing by informational self-determination plays a
key role on the privacy-utility trajectories as shown in this paper both
theoretically and empirically. A generic and novel computational framework is
introduced for measuring privacy-utility trade-offs and their optimization. The
framework computes a broad spectrum of such trade-offs that form
privacy-utility trajectories under homogeneous and heterogeneous data sharing.
