Summary
Objectives:
Current genomic privacy technologies assume the identity of genomic sequence data
is protected if personal information, such as demographics, are obscured, removed,
or encrypted. While demographic features can directly compromise an individual’s identity,
recent research demonstrates such protections are insufficient because sequence data
itself is susceptible to re-identification. To counteract this problem, we introduce
an algorithm for anonymizing a collection of person-specific DNA sequences.
Methods:
The technique is termed DNA lattice an-onymization (DNALA), and is based upon the
formal privacy protection schema of k-anonymity. Under this model, it is impossible to observe or learn features that distinguish
one genetic sequence from k-1 other entries in a collection. To maximize information retained in protected sequences,
we incorporate a concept generalization lattice to learn the distance between two
residues in a single nucleotide region. The lattice provides the most similar generalized
concept for two residues (e.g. adenine and guanine are both purines).
Results:
The method is tested and evaluated with several publicly available human population
datasets ranging in size from 30 to 400 sequences. Our findings imply the anonymization
schema is feasible for the protection of sequences privacy.
Conclusions:
The DNALA method is the first computational disclosure control technique for general
DNA sequences. Given the computational nature of the method, guarantees of anonymity
can be formally proven. There is room for improvement and validation, though this
research provides the groundwork from which future researchers can construct genomics
anonymization schemas tailored to specific data-sharing scenarios.
Keywords
Privacy - anonymity - databases - genetic variation - genomic data - sequence analysis
