  Database fingerprinting schemes have been widely adopted to prevent
unauthorized sharing of data and identify the source of data leakages. Although
existing schemes are robust against common attacks, such as random bit flipping
and subset attack, their robustness degrades significantly if attackers utilize
the inherent correlations among database entries. In this paper, we first
demonstrate this vulnerability of the existing database fingerprinting schemes
by identifying different correlation attacks: column-wise correlation attack,
row-wise correlation attack, and the integration of them. To provide robust
fingerprinting against the identified correlation attacks, we then develop
mitigation techniques, which can work as post-processing steps for any
off-the-shelf database fingerprinting schemes. The proposed mitigation
techniques also preserve the utility of the fingerprinted database considering
different utility metrics. We empirically investigate the impact of the
identified correlation attacks and the performance of mitigation techniques
using two real-world relational databases. Our results show (i) high success
rates of the identified correlation attacks against existing fingerprinting
schemes (e.g., the integrated correlation attack can distort 64.8% fingerprint
bits by just modifying 14.2% entries in a fingerprinted database), and (ii)
high robustness of the proposed mitigation techniques (e.g., after the proposed
mitigation techniques, the integrated correlation attack can only distort 3%
fingerprint bits). Furthermore, we show that the proposed mitigation techniques
effectively alleviate correlation attacks even if the database owner has less
accurate knowledge about data correlations compared to the attacker.
