2 research outputs found
ACE: A Consent-Embedded privacy-preserving search on genomic database
In this paper, we introduce ACE, a consent-embedded searchable encryption
scheme. ACE enables dynamic consent management by supporting the physical
deletion of associated data at the time of consent revocation. This ensures
instant real deletion of data, aligning with privacy regulations and preserving
individuals' rights. We evaluate ACE in the context of genomic databases,
demonstrating its ability to perform the addition and deletion of genomic
records and related information based on ID, which especially complies with the
requirements of deleting information of a particular data owner. To formally
prove that ACE is secure under non-adaptive attacks, we present two new
definitions of forward and backward privacy. We also define a new hard problem,
which we call D-ACE, that facilitates the proof of our theorem (we formally
prove its hardness by a security reduction from DDH to D-ACE). We finally
present implementation results to evaluate the performance of ACE
PrivGenDB: Efficient and privacy-preserving query executions over encrypted SNP-Phenotype database
Searchable symmetric encryption (SSE) has been used to protect the
confidentiality of genomic data while providing substring search and range
queries on a sequence of genomic data, but it has not been studied for
protecting single nucleotide polymorphism (SNP)-phenotype data. In this
article, we propose a novel model, PrivGenDB, for securely storing and
efficiently conducting different queries on genomic data outsourced to an
honest-but-curious cloud server. To instantiate PrivGenDB, we use SSE to ensure
confidentiality while conducting different types of queries on encrypted
genomic data, phenotype and other information of individuals to help
analysts/clinicians in their analysis/care. To the best of our knowledge,
PrivGenDB construction is the first SSE-based approach ensuring the
confidentiality of shared SNP-phenotype data through encryption while making
the computation/query process efficient and scalable for biomedical research
and care. Furthermore, it supports a variety of query types on genomic data,
including count queries, Boolean queries, and k'-out-of-k match queries.
Finally, the PrivGenDB model handles the dataset containing both genotype and
phenotype, and it also supports storing and managing other metadata like gender
and ethnicity privately. Computer evaluations on a dataset with 5,000 records
and 1,000 SNPs demonstrate that a count/Boolean query and a k'-out-of-k match
query over 40 SNPs take approximately 4.3s and 86.4{\mu}s, respectively, that
outperforms the existing schemes