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