Secure Count Query on Encrypted Genomic Data: A Survey

Hasan, Zahidul; Mahdi, Safiur Rahman; Mohammed, Noman

doi:10.1109/mic.2018.112102323

Cited by 6 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We utilized the cloud services from Amazon AWS m4.xlarge instances (4 CPUs, 16GB memory, 500GB disk space) to store and perform the required computations. Furthermore, for comparison with the earlier works [14, 15], we executed these algorithm with 7 patients and 736K SNPs in the same environment. Unfortunately, the programs ended with ‘Stack Overflow’ error during the early phase of encryption process.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, we utilized a homomorphic cryptographic combined with garbled circuit scheme to ensure the security and tree structure to represent the arbitrary genome data for computational efficiency. The major contributions of the paper can be summarized below: We propose a method utilizing graph-based database to store and allow computations on real-world genome data securely .A novel indexing scheme is proposed on such database to make the secure query operations more efficient.We test the proposed approach along with the corresponding indexing scheme on a large-scale genome dataset containing 735, 317 human SNPs (~ 200 GB data).Experimental results show that it takes less than a minute for a query compared to best-known attempts where it required around 7 minutes [14, 15]. …”

Section: Introductionmentioning

confidence: 99%

“…Experimental results show that it takes less than a minute for a query compared to best-known attempts where it required around 7 minutes [14, 15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Secure large-scale genome data storage and query

Chen

Aziz

Mohammed

et al. 2018

Computer Methods and Programs in Biomedicine

Self Cite

View full text Add to dashboard Cite

Background and Objective Cloud computing plays a vital role in big data science with its scalable and cost-efficient architecture. Largescale genome data storage and computations would benefit from using these latest cloud computing infrastructures, to save cost and speedup discoveries. However, due to the privacy and security concerns, data owners are often disinclined to put sensitive data in a public cloud environment without enforcing some protective measures. An ideal solution is to develop secure genome database that supports encrypted data deposition and query. Methods Nevertheless, it is a challenging task to make such a system fast and scalable enough to handle real-world demands providing data security as well. In this paper, we propose a novel, secure mechanism to support secure count queries on an open source graph database (Neo4j) and evaluated the performance on a realworld dataset of around 735,317 Single Nucleotide Polymorphisms (SNPs). In particular, we propose a new tree indexing method that offers constant time complexity (proportion to the tree depth), which was the bottleneck of existing approaches. Results The proposed method significantly improves the runtime of query execution compared to the existing techniques. It takes less than one minute to execute an arbitrary count query on a dataset of 212 GB, while the best-known algorithm takes around 7 minutes. Conclusions The outlined framework and experimental results show the applicability of utilizing graph database for securely storing large-scale genome data in untrusted environment. Furthermore, the crypto-system and security assumptions underlined are much suitable for such use cases which be generalized in future work.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Secure large-scale genome data storage and query

Chen

Aziz

Mohammed

et al. 2018

Computer Methods and Programs in Biomedicine

Self Cite

View full text Add to dashboard Cite

show abstract

“…In biomedicine and GWAS in particular, cryptographic techniques have been used to collaboratively compute result statistics while preserving data privacy. 40 44 45 46 47 48 49 50 51 52 53 54 55 56 These cryptographic approaches are based on HE 57 58 59 or SMPC. 60 There are different HE-based techniques such as partially HE (PHE) 58 and fully HE (FHE).…”

Section: Cryptographic Techniquesmentioning

confidence: 99%

Privacy-Preserving Artificial Intelligence Techniques in Biomedicine

et al. 2022

View full text Add to dashboard Cite

Background Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g., in the interpretation of next-generation sequencing data and in the design of clinical decision support systems. Objectives However, training an AI model on sensitive data raises concerns about the privacy of individual participants. For example, summary statistics of a genome-wide association study can be used to determine the presence or absence of an individual in a given dataset. This considerable privacy risk has led to restrictions in accessing genomic and other biomedical data, which is detrimental for collaborative research and impedes scientific progress. Hence, there has been a substantial effort to develop AI methods that can learn from sensitive data while protecting individuals' privacy. Method This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy and discusses their strengths, limitations, and open problems. Conclusion As the most promising direction, we suggest combining federated machine learning as a more scalable approach with other additional privacy-preserving techniques. This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications. Nonetheless, more research is necessary as hybrid approaches pose new challenges such as additional network or computation overhead.

show abstract

“…Readers are recommended to read surveys in [16], [2], [17], [18], [19] and [20] for more information on genomic privacy.…”

Section: A Related Workmentioning

confidence: 99%

An Active Genomic Data Recovery Attack

Akgün

2019

Balkan Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

With the decreasing cost and availability of human genome sequencing, genomic privacy becomes an important issue. Several methods have been proposed in the literature to overcome these problems including cryptographic and privacypreserving data mining methods: homomorphic encryption, cryptographic hardware. In recent work, Barman et. al studied privacy threats and practical solutions considering an SNP based scenario. The authors introduced a new protocol where a malicious medical center processes an active attack in order to retrieve genomic data of a given patient. The authors have mentioned that this protocol provides a trade-off between privacy and practicality. In this paper, we first give an overview of the system for SNP based risk calculation. We provide the definitions of privacy threats and briefly Barman et al.'s protocol and solution. The authors proposed to use a weighted sum of SNP coefficients for calculating disease tendency. They argue that the specific choice of the bases would prevent unique identification of SNPs. Our main observation is that this is not true. Contrary to the security claim, SNP combinations can be identified uniquely in many different scenarios. Our method exploits a pre-computed look-up table for retrieving SNPs' values from the test result. An attacker can obtain all SNP values of a given patient by using the pre-computed look-up table. We provide practical examples of weights and pre-computed tables. We also mention that even in the case where the table is large and the attacker can not handle it at one time, he can still gather information using multi queries. Our work shows that more realistic attack scenarios must be considered in the design of genetic security systems.

show abstract

Secure Count Query on Encrypted Genomic Data: A Survey

Cited by 6 publications

References 15 publications

Secure large-scale genome data storage and query

Secure large-scale genome data storage and query

Privacy-Preserving Artificial Intelligence Techniques in Biomedicine

An Active Genomic Data Recovery Attack

Contact Info

Product

Resources

About