  Online mobile advertising ecosystems provide advertising and analytics
services that collect, aggregate, process, and trade a rich amount of
consumers' personal data and carry out interest-based ad targeting, which
raised serious privacy risks and growing trends of users feeling uncomfortable
while using the internet services. In this paper, we address users' privacy
concerns by developing an optimal dynamic optimisation cost-effective framework
for preserving user privacy for profiling, ads-based inferencing, temporal apps
usage behavioral patterns, and interest-based ad targeting. A major challenge
in solving this dynamic model is the lack of knowledge of time-varying updates
during the profiling process. We formulate a mixed-integer optimisation problem
and develop an equivalent problem to show that the proposed algorithm does not
require knowledge of time-varying updates in user behavior. Following, we
develop an online control algorithm to solve the equivalent problem and
overcome the difficulty of solving nonlinear programming by decomposing it into
various cases and to achieve a trade-off between user privacy, cost, and
targeted ads. We carry out extensive experimentations and demonstrate the
proposed framework's applicability by implementing its critical components
using POC (Proof Of Concept) `System App'. We compare the proposed framework
with other privacy-protecting approaches and investigate whether it achieves
better privacy and functionality for various performance parameters.
