  In order to receive personalized services, individuals share their personal
data with a wide range of service providers, hoping that their data will remain
confidential. Thus, in case of an unauthorized distribution of their personal
data by these service providers (or in case of a data breach) data owners want
to identify the source of such data leakage. Digital fingerprinting schemes
have been developed to embed a hidden and unique fingerprint into shared
digital content, especially multimedia, to provide such liability guarantees.
However, existing techniques utilize the high redundancy in the content, which
is typically not included in personal data. In this work, we propose a
probabilistic fingerprinting scheme that efficiently generates the fingerprint
by considering a fingerprinting probability (to keep the data utility high) and
publicly known inherent correlations between data points. To improve the
robustness of the proposed scheme against colluding malicious service
providers, we also utilize the Boneh-Shaw fingerprinting codes as a part of the
proposed scheme. Furthermore, observing similarities between privacy-preserving
data sharing techniques (that add controlled noise to the shared data) and the
proposed fingerprinting scheme, we make a first attempt to develop a data
sharing scheme that provides both privacy and fingerprint robustness at the
same time. We experimentally show that fingerprint robustness and privacy have
conflicting objectives and we propose a hybrid approach to control such a
trade-off with a design parameter. Using the proposed hybrid approach, we show
that individuals can improve their level of privacy by slightly compromising
from the fingerprint robustness. We implement and evaluate the performance of
the proposed scheme on real genomic data. Our experimental results show the
efficiency and robustness of the proposed scheme.
