Biblio

In recent years, there is fast growth in the high throughput DNA sequencing technology, and also there is a reduction in the cost of genome-sequencing, that has led to a advances in the genetic industries. However, the reduction in cost and time required for DNA sequencing there is still an issue of managing such large amount of data. Also, the security and transmission of such huge amount of DNA sequence data is still an issue. The idea is to provide a secure storage platform for future generation bioinformatics systems for both researchers and healthcare user. Secure data sharing strategies, that can permit the healthcare providers along with their secured substances for verifying the accuracy of data, are crucial for ensuring proper medical services. In this paper, it has been surveyed about the applications of blockchain technology for securing healthcare data, where the recorded information is encrypted so that it becomes difficult to penetrate or being removed, as the primary goals of block-chaining technology is to make data immutable.

Digital microfluidic biochips (DMFBs) have become popular in the healthcare industry recently because of its lowcost, high-throughput, and portability. Users can execute the experiments on biochips with high resolution, and the biochips market therefore grows significantly. However, malicious attackers exploit Intellectual Property (IP) piracy and Trojan attacks to gain illegal profits. The conventional approaches present defense mechanisms that target either IP piracy or Trojan attacks. In practical, DMFBs may suffer from the threat of being attacked by these two attacks at the same time. This paper presents a comprehensive security system to protect DMFBs from IP piracy and Trojan attacks. We propose an authentication mechanism to protect IP and detect errors caused by Trojans with CCD cameras. By our security system, we could generate secret keys for authentication and determine whether the bioassay is under the IP piracy and Trojan attacks. Experimental results demonstrate the efficacy of our security system without overhead of the bioassay completion time.

Data security is a major requirement of smart meter communication to control server through Advanced Metering infrastructure. Easy access of smart meters and multi-faceted nature of AMI communication network are the main reasons of smart meter facing large number of attacks. The different topology, bandwidth and heterogeneity in communication network prevent the existing security mechanisms in satisfying the security requirements of smart meter. Hence, advanced security mechanisms are essential to encrypt smart meter data before transmitting to control server. The emerging biocryptography technique has several advantages over existing techniques and is most suitable for providing security to communication of low processing devices like smart meter. In this paper, a lightweight encryption scheme using DNA sequence with suitable key management scheme is proposed for secure communication of smart meter in an efficient way. The proposed 2-phase DNA cryptography provides confidentiality and integrity to transmitted data and the authentication of keys is attained by exchanging through Diffie Hellman scheme. The strength of proposed encryption scheme is analyzed and its efficiency is evaluated by simulating an AMI communication network using Simulink/Matlab. Comparison of simulation results with various techniques show that the proposed scheme is suitable for secure communication of smart meter data.

Personalized medicine performs diagnoses and treatments according to the DNA information of the patients. The new paradigm will change the health care model in the future. A doctor will perform the DNA sequence matching instead of the regular clinical laboratory tests to diagnose and medicate the diseases. Additionally, with the help of the affordable personal genomics services such as 23andMe, personalized medicine will be applied to a great population. Cloud computing will be the perfect computing model as the volume of the DNA data and the computation over it are often immense. However, due to the sensitivity, the DNA data should be encrypted before being outsourced into the cloud. In this paper, we start from a practical system model of the personalize medicine and present a solution for the secure DNA sequence matching problem in cloud computing. Comparing with the existing solutions, our scheme protects the DNA data privacy as well as the search pattern to provide a better privacy guarantee. We have proved that our scheme is secure under the well-defined cryptographic assumption, i.e., the sub-group decision assumption over a bilinear group. Unlike the existing interactive schemes, our scheme requires only one round of communication, which is critical in practical application scenarios. We also carry out a simulation study using the real-world DNA data to evaluate the performance of our scheme. The simulation results show that the computation overhead for real world problems is practical, and the communication cost is small. Furthermore, our scheme is not limited to the genome matching problem but it applies to general privacy preserving pattern matching problems which is widely used in real world.

Genetic data are important dataset utilised in genetic epidemiology to investigate biologically coded information within the human genome. Enormous research has been delved into in recent years in order to fully sequence and understand the genome. Personalised medicine, patient response to treatments and relationships between specific genes and certain characteristics such as phenotypes and diseases, are positive impacts of studying the genome, just to mention a few. The sensitivity, longevity and non-modifiable nature of genetic data make it even more interesting, consequently, the security and privacy for the storage and processing of genomic data beg for attention. A common activity carried out by geneticists is the association analysis between allele-allele, or even a genetic locus and a disease. We demonstrate the use of cryptographic techniques such as homomorphic encryption schemes and multiparty computations, how such analysis can be carried out in a privacy friendly manner. We compute a 3 × 3 contingency table, and then, genome analyses algorithms such as linkage disequilibrium (LD) measures, all on the encrypted domain. Our computation guarantees privacy of the genome data under our security settings, and provides up to 98.4% improvement, compared to an existing solution.

Genes, proteins and other metabolites present in cellular environment exhibit a virtual network that represents the regulatory relationship among its constituents. This network is called Gene Regulatory Network (GRN). Computational reconstruction of GRN reveals the normal metabolic pathway as well as disease motifs. Availability of microarray gene expression data from normal and diseased tissues makes the job easier for computational biologists. Reconstruction of GRN is based on neural modeling. Here we have used discrete and continuous versions of a meta-heuristic algorithm named Firefly algorithm for structure and parameter learning of GRNs respectively. The discrete version for this problem is proposed by us and it has been applied to explore the discrete search space of GRN structure. To evaluate performance of the algorithm, we have used a widely used synthetic GRN data set. The algorithm shows an accuracy rate above 50% in finding GRN. The accuracy level of the performance of Firefly algorithm in structure and parameter optimization of GRN is promising.