Quantitative Evaluation of Unlinkable ID Matching Schemes

As pervasive computing environments become popular, RFID devices, such as contactless smart cards and RFID tags, are introduced into our daily life. However, there exists a privacy problem that a third party can trace user’s behavior by linking device’s ID. The concept of unlinkability, that a third party cannot recognize whether some outputs are from the same user, is important to solve the privacy problem. A scheme using hash function satisfies unlinkability against a third party by changing the outputs of RFID devices every time. However, the schemes are not scalable since the server needs O(N) hash calculations for every ID matching, where N is the number of RFID devices. In this paper, we propose the K-steps ID matching scheme, which can reduce the number of the hash calculations on the server to O(logN). Secondly, we propose a quantification of unlinkability using conditional entropy and mutual information. Finally, we analyze the K-steps ID matching scheme using the proposed quantification, and show the relation between the time complexity and unlinkability

Quantitative Evaluation of Unlinkable ID Matching Schemes

As pervasive computing environments become popular, RFID devices, such as contactless smart cards and RFID tags, are introduced into our daily life. However, there exists a privacy problem that a third party can trace user’s behavior by linking device’s ID. The concept of unlinkability, that a third party cannot recognize whether some outputs are from the same user, is important to solve the privacy problem. A scheme using hash function satisfies unlinkability against a third party by changing the outputs of RFID devices every time. However, the schemes are not scalable since the server needs O(N) hash calculations for every ID matching, where N is the number of RFID devices. In this paper, we propose the K-steps ID matching scheme, which can reduce the number of the hash calculations on the server to O(logN). Secondly, we propose a quantification of unlinkability using conditional entropy and mutual information. Finally, we analyze the K-steps ID matching scheme using the proposed quantification, and show the relation between the time complexity and unlinkability

ABSTRACT Quantitative Evaluation of Unlinkable ID Matching Schemes

As pervasive computing environments become popular, RFID devices, such as contactless smart cards and RFID tags, are introduced into our daily life. However, there exists a privacy problem that a third party can trace user’s behavior by linking device’s ID. The concept of unlinkability, that a third party cannot recognize whether some outputs are from the same user, is important to solve the privacy problem. A scheme using hash function satisfies unlinkability against a third party by changing the outputs of RFID devices every time. However, the schemes are not scalable since the server needs O(N) hash calculations for every ID matching, where N is the number of RFID devices. In this paper, we propose the K-steps ID matching scheme, which can reduce the number of the hash calculations on the server to O(log N). Secondly, we propose a quantification of unlinkability using conditional entropy and mutual information. Finally, we analyze the K-steps ID matching scheme using the proposed quantification, and show the relation between the time complexity and unlinkability