  A personal data market is a platform including three participants: data
owners (individuals), data buyers and market maker. Data owners who provide
personal data are compensated according to their privacy loss. Data buyers can
submit a query and pay for the result according to their desired accuracy.
Market maker coordinates between data owner and buyer. This framework has been
previously studied based on differential privacy. However, the previous study
assumes data owners can accept any level of privacy loss and data buyers can
conduct the transaction without regard to the financial budget. In this paper,
we propose a practical personal data trading framework that is able to strike a
balance between money and privacy. In order to gain insights on user
preferences, we first conducted an online survey on human attitude to- ward
privacy and interest in personal data trading. Second, we identify the 5 key
principles of personal data market, which is important for designing a
reasonable trading frame- work and pricing mechanism. Third, we propose a
reason- able trading framework for personal data which provides an overview of
how the data is traded. Fourth, we propose a balanced pricing mechanism which
computes the query price for data buyers and compensation for data owners
(whose data are utilized) as a function of their privacy loss. The main goal is
to ensure a fair trading for both parties. Finally, we will conduct an
experiment to evaluate the output of our proposed pricing mechanism in
comparison with other previously proposed mechanism.
