7 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
A Survey on Exotic Signatures for Post-Quantum Blockchain: Challenges & Research Directions
Blockchain technology provides efficient and secure solutions to various online activities by utilizing a wide range of cryptographic tools. In this paper, we survey the existing literature on post-quantum secure digital signatures that possess exotic advanced features and which are crucial cryptographic tools used in the blockchain ecosystem for (i) account management, (ii) consensus efficiency, (iii) empowering scriptless blockchain, and (iv) privacy. The exotic signatures that we particularly focus on in this work are the following: multi-/aggregate, threshold, adaptor, blind and ring signatures. Herein the term exotic refers to signatures with properties which are not just beyond the norm for signatures e.g. unforgeability, but also imbue new forms of functionalities. Our treatment of such exotic signatures includes discussions on existing challenges and future research directions in the post-quantum space. We hope that this article will help to foster further research to make post-quantum cryptography more accessible so that blockchain systems can be made ready in advance of the approaching quantum threats
A Multi-Client Searchable Encryption Scheme for IoT Environment
The proliferation of connected devices through Internet connectivity presents
both opportunities for smart applications and risks to security and privacy. It
is vital to proactively address these concerns to fully leverage the potential
of the Internet of Things. IoT services where one data owner serves multiple
clients, like smart city transportation, smart building management and
healthcare can offer benefits but also bring cybersecurity and data privacy
risks. For example, in healthcare, a hospital may collect data from medical
devices and make it available to multiple clients such as researchers and
pharmaceutical companies. This data can be used to improve medical treatments
and research but if not protected, it can also put patients' personal
information at risk. To ensure the benefits of these services, it is important
to implement proper security and privacy measures. In this paper, we propose a
symmetric searchable encryption scheme with dynamic updates on a database that
has a single owner and multiple clients for IoT environments. Our proposed
scheme supports both forward and backward privacy. Additionally, our scheme
supports a decentralized storage environment in which data owners can outsource
data across multiple servers or even across multiple service providers to
improve security and privacy. Further, it takes a minimum amount of effort and
costs to revoke a client's access to our system at any time. The performance
and formal security analyses of the proposed scheme show that our scheme
provides better functionality, and security and is more efficient in terms of
computation and storage than the closely related works.Comment: 22 pages, 5 figures, this version was submitted to ESORICS 202