Big Data Privacy in Biomedical Research || JAVA IEEE PROJECT

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Abstract:
Biomedical research often involves studying patient data that contain personal information.Inappropriate use of these data might lead to leakage of sensitive information, which can put patient privacy at risk. The problem of preserving patient privacy has received increasing attentions in the era of big data. Many privacy methods have been developed to protect against various attack models. This paper reviews relevant topics in the context of biomedical research. We discuss privacy preserving technologies related to (1) record linkage, (2) synthetic data generation, and (3) genomic data privacy. We also discuss the ethical implications of big data privacy in biomedicine and present challenges in future research directions for improving data privacy in biomedical research.
EXISTING SYSTEM:
• Homomorphic Encryption (HME)allows the direct computation over encrypted data using certain arithmetic operations (i.e.,multiplication and addition), where the returned output is also encrypted under the same encryption key.
• Three are different types of HME cryptosystems:
• partially HME allows a single type of HME operation (e.g.,either addition or multiplication),
• somewhat HME enables both HME operations with a limited number of iterations and additional computational costs
• fully HME supports unlimited number of both operations with considerable computational costs.
• Secure multiparty computation (SMC) is a set of cryptographic protocols that enable two or more parties to jointly compute functions over their private inputs without leaking their sensitive information. Garbled Circuit is widely used to achieve secure two-party computation. In a garbled circuit, the inputs of each gate will be mapped to garbled values, and the truth table of each gate will be encrypted by these garbled inputs.

DISADVANTAGES OF EXISTING SYSTEM:

• Could lead to information disclosure and privacy breach and will negatively impact patients and may have serious implications (e.g., discrimination for employment, insurance, or education).
• Some think that protections from data de-idientification are not sufficient.
• Current privacy rules do not deal with longitudinal data and transactional data, which can be used to re-identify an individual.

PROPOSED SYSTEM:

• We selected a few important and practical topics in biomedical research to discuss related privacy preserving technologies. These topics include: (1) record linkage, (2) distributed data analysis, (3) synthetic data generation, and (4) secure genome analyses.
• We will focus on both privacy protection technologies for both electronic health records (EHR) and genomic data.

ADVANTAGES OF PROPOSED SYSTEM:
• The outcomes of the competitions identified several limitations in the current genome privacy protection studies.First, genomic data protection using perturbation-based protection methods often present too much noise, where practical genomic applications may not be able to trust worthy rely on these noise outputs.
• Second,cryptography-based protection methods for secure collaboration or outsourcing currently only support limited genomic computations due to their complexity.
• Third, the ethical implications of these protection methods are not yet clear. Therefore, further investigations of genome privacy are important and necessary,which motivates researchers to develop advanced genomeprivacy protection technologies and to investigate their ethical implications.