  A large amount of transaction data containing associations between
individuals and sensitive information flows everyday into data stores. Examples
include web queries, credit card transactions, medical exam records, transit
database records. The serial release of these data to partner institutions or
data analysis centers is a common situation. In this paper we show that, in
most domains, correlations among sensitive values associated to the same
individuals in different releases can be easily mined, and used to violate
users' privacy by adversaries observing multiple data releases. We provide a
formal model for privacy attacks based on this sequential background knowledge,
as well as on background knowledge on the probability distribution of sensitive
values over different individuals. We show how sequential background knowledge
can be actually obtained by an adversary, and used to identify with high
confidence the sensitive values associated with an individual. A defense
algorithm based on Jensen-Shannon divergence is proposed, and extensive
experiments show the superiority of the proposed technique with respect to
other applicable solutions. To the best of our knowledge, this is the first
work that systematically investigates the role of sequential background
knowledge in serial release of transaction data.
