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Mhaske and Kumar

to as “BDSDT.” In particular, by utilizing the Zero vulnerable, where hackers and other unapproved
Knowledge Proof technique, a unique adaptable BT was parties can readily access the data. This vulnerability
suggested to guarantee confidentiality as well as safe not only compromises patient data but restricts access
data transfer. To identify intrusions in the HS network, a for patients and health-care professionals. The existing
DL method was designed using the verified data. Then, approaches are unable to strike a compromise between
an efficient intrusion detection system was created by data accessibility and security. However, BT offers
combining bidirectional long short-term memory with a promising solution to these problems. Blockchain
deep sparse autoencoder. establishes an immutable ledger system that enables
The rapid adoption of ubiquitous computing and decentralized transaction processing. A novel PP
mobile communication has led to the emergence technique is proposed for securing EHR, with stages
of mobile health, while urbanization has driven the shown in Figure 1.
development of smart cities. The concept of smart Initially, the improved ARM approach is used to
health integrates these two trends, creating a context- analyze medical data and find the ASRs between the
aware health-care system within smart cities to data characteristics. These rules are important since
enhance service efficiency and human-centered care. they aid in identifying sensitive items in the dataset.
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Context-aware health-care systems that utilize context Improvement in ARM approaches improves the process
awareness in smart health-care applications emphasize and guarantees a more precise identification of patterns
the importance of user demographics, location, and in sensitive data. After identifying the ASRs, the SBI-
medical history, which aligns with the concept of smart TSA method is developed to identify the optimal keys,
health within smart cities. With the rapid development which are employed to encrypt and decode sensitive
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of machine learning for the detection of diseases using data. These optimal keys are derived by considering
imaging scans, 42,43 patient records have become an IPR, HFR, and MD.
invaluable resource. It facilitates diagnostics, leading to The sensitive data are subjected to an exclusive OR
the development of artificial intelligence-based medical (XOR) operation for sanitization using the optimal keys.
techniques. EHRs are simpler to access and manage This sanitized data is then stored on a blockchain. This
than paper records, but more caution is needed to ensure phase ensures that the stored data cannot be decrypted
that the privacy of the data is maintained. Because of without the matching key, even in the event of illegal
their centralized architecture, traditional and modern access. When retrieval is necessary, the sanitized data
EHR systems, which are utilized for exchanging data undergoes a reverse XOR process with the optimal
between medical participants (patients, doctors, insurers, key, restoring it to its original format. This decryption
pharmaceuticals, doctors, and researchers), have security process allows authorized individuals to safely access
and privacy flaws. To prevent breaches of information and use the information.
privacy, several clinics and institutions have prohibited
medical data transfer and exchange. Data barriers have 4. Data sanitization using improved ARM
arisen as a consequence of health data being dispersed
among several health-care providers, exacerbated by Assume the medical dataset as d and D as the data
s
t
concerns over health-care data security and privacy. As within the dataset. This medical data D (D = {D ,
1
t
i
a result, BT is proposed as a solution, using encryption D , … D }) is initially processed using the improved
n
2
to guarantee the security and privacy of EHR systems. ARM. Data sanitization is vital for conserving privacy
BT overcomes the limitations of traditional centralized by sanitizing data D . Sanitization includes precisely
t
systems, which are often inaccessible. The existing identifying the sensitive information within the data to
health-care system is perceived as complicated and protect data privacy while preserving the data validity.
expensive, but BT can mitigate these issues by enhancing
insurance management and data handling. Furthermore, 4.1. Conventional ARM
the decentralized nature of this system eliminates central ARM analyzes input medical data D to find significant
t
attack points and reduces the risk of system failures. correlations or links between different elements,
including diseases, therapies, indications, and other
3. PP of EHRs using BT factors. By finding important patterns in huge datasets,
this approach seeks to provide insights that might
EHRs are digital patient records stored on networks. improve patient care and decision-making. These
Still, current storage methods have proven to be quite methods selectively hide some ASRs that may otherwise

Volume 22 Issue 1 (2025) 152 doi: 10.36922/AJWEP025040017