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Privacy in Internet of Healthcare Things
Published in Ahmed Elngar, Ambika Pawar, Prathamesh Churi, Data Protection and Privacy in Healthcare, 2021
Mohammad Wazid, Ashok Kumar Das
Biometrics data contain a user’s physical attributes which vary from person to person with a trivial collision rate. The various kinds of physical attributes which can be used as “biometrics for authentication” are iris scans, fingerprints, face recognition, etc. For biometrics-based authentication the following techniques can be used for verification, which are discussed below. Biometric verification using one-way cryptographic hash function: If we apply the one-way hash function on a user’s personal biometric template (for instance, fingerprint), the hash value on the input biometric may completely differ with a slight change in the user’s biometrics at the time of biometric verification. As a result, this technique will produce high rates of false alarms. Thus, one-way hash function-based biometric verification is not considered reliable.Biometric verification using biohashing function: This technique operates on a user’s personal biometrics for unique identification to reduce the “false denial of access” without increasing the “false acceptance” [54–57]. Various biohashing-based algorithms have been designed in recent years which make biohashing more useful for applications including in small devices (i.e., sensors and other mobile devices). However, Chang et al. [58] observed an important problem with biometric verification using biohashing function, because it produces a “high rate of false rejection.” As a result, the biohashing function may not be considered as a “good candidate for biometric verification.”Biometric verification using fuzzy extractor: To overcome the problems with the “one- way cryptographic hash function” and “biohashing” techniques, a fuzzy extractor technique is preferred in biometric verification procedures which are applied in authentication techniques. The fuzzy extractor method mutates biometric data into random strings to facilitate the cryptographic applications to use biometrics as a secret key for verification purposes. The fuzzy extractor then deduces a uniform random string from the provided biometric data BIO which can tolerate noise up to a certain range. The input BIO also results in the same random string in case BIO is close to the original BIO [53].
Enhanced ECC Based Authentication Protocol in Wireless Sensor Network with DoS Mitigation
Published in Cybernetics and Systems, 2022
Sobini X. Pushpa, S. Kanaga Suba Raja
Table 1 presents the review on traditional authentication protocols. At first, fuzzy extractor (Soni, Pal, and Islam 2019) has resisted active and passive attacks with high security; however, it suffers from higher processing cost. CHAP model (Vengala, Kavitha, and Kumar 2021) improves the compression ratio and minimizes the compression time. However, there is no consideration on distributed CS. Moreover, fuzzy extractor was employed in Ali et al. (2020) that offer reduced overhead and better security. Nevertheless, it needs further concern in reducing computation time. Bilinear mapping function was exploited in Ramachandran and Shanmugam (2017) that minimizes overhead with least energy utilization; however, it requires extension on cloud environments. Fuzzy commitment algorithm (Li et al. 2020) ensures reduced time cost with increased robustness; however, it should focus on complexity. BAN logic is used in Alshudukhi, Mohammed, and Al-Mekhlafi (2020) offers minimal overhead with minimal computational cost. However, it needs exploration on time consumption. ECDSA adopted in Qazi et al. (2021) requires minimal bandwidth and can be applied in real world environments. Nevertheless, it needs exploration on deployment time. In addition, 1b scheme was adopted in Singh, Awasthi, and Singh (2017), which offered high security and reduced overhead. However, it has to focus against collisions.