2021
DOI: 10.1007/s11227-021-03656-0
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Constructing effective lightweight privacy protection in RFID-based systems

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Cited by 5 publications
(5 citation statements)
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“…In 2021, Chuang and Tu, after examining the shortcomings of Fan's protocol, introduced a lightweight RFID authentication protocol to overcome the security and privacy problems of IoT systems and claimed that it was very fast and efficient. They also claimed that their protocol was resistant to all RFID attacks, such as tag impersonation, reader impersonation and de −synchronisation [11]. They tried to strike a cost-security balance by using small-scale and simple operations, such as XCor(x,y), LRot(x,y) and XOR.…”
Section: Review Of the Chuang Authentication Protocolmentioning
confidence: 99%
See 3 more Smart Citations
“…In 2021, Chuang and Tu, after examining the shortcomings of Fan's protocol, introduced a lightweight RFID authentication protocol to overcome the security and privacy problems of IoT systems and claimed that it was very fast and efficient. They also claimed that their protocol was resistant to all RFID attacks, such as tag impersonation, reader impersonation and de −synchronisation [11]. They tried to strike a cost-security balance by using small-scale and simple operations, such as XCor(x,y), LRot(x,y) and XOR.…”
Section: Review Of the Chuang Authentication Protocolmentioning
confidence: 99%
“…According to its developers, the protocol is resistant to all common attacks on RFID systems. Recently, however, Chuang and Tu [11] have shown that Fan's protocol is vulnerable to eavesdropping, tag impersonation and tag tracking attacks. Chuang and Tu assessed Fan's protocol and attempted to fix its shortcomings with a modified version.…”
Section: Related Workmentioning
confidence: 99%
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“…Several strategies have been suggested to address privacy and security concerns in DL. 14 There are four primary mechanisms employed to preserve the privacy of DL such as differential privacy, homomorphic encryption, secure multi-party computation, and trusted execution environment. By using differential privacy, the adversary is prevented from deducing whether a certain instance was exploited to train the target model.…”
Section: Introductionmentioning
confidence: 99%