Cancelable remote quantum fingerprint templates protection scheme

With the increasing popularity of fingerprint identification technology, its security and privacy have been paid much attention. Only the security and privacy of biological information are insured, the biological technology can be better accepted and used by the public. In this paper, we propose a novel quantum bit（qbit）-based scheme to solve the security and privacy problem existing in the traditional fingerprint identification system. By exploiting the properties of quantum mechanics, our proposed scheme, cancelable remote quantum fingerprint templates protection scheme, can achieve the unconditional security guaranteed in an information-theoretical sense. Moreover, this novel quantum scheme can invalidate most of the attacks aimed at the fingerprint identification system. In addition, the proposed scheme is applicable to the requirement of remote communication with no need to worry about its security and privacy during the transmission. This is an absolute advantage when comparing with other traditional methods. Security analysis shows that the proposed scheme can effectively ensure the communication security and the privacy of users＇ information for the fingerprint identification.

An Active De-anonymizing Attack Against Tor Web Traffic

Tor is pervasively used to conceal target websites that users are visiting. A de-anonymization technique against Tor, referred to as website fingerprinting attack, aims to infer the websites accessed by Tor clients by passively analyzing the patterns of encrypted traffic at the Tor client side. However, HTTP pipeline and Tor circuit multiplexing techniques can affect the accuracy of the attack by mixing the traffic that carries web objects in a single TCP connection. In this paper, we propose a novel active website fingerprinting attack by identifying and delaying the HTTP requests at the first hop Tor node. Then, we can separate the traffic that carries distinct web objects to derive a more distinguishable traffic pattern. To fulfill this goal, two algorithms based on statistical analysis and objective function optimization are proposed to construct a general packet delay scheme. We evaluate our active attack against Tor in empirical experiments and obtain the highest accuracy of 98.64%, compared with 85.95% of passive attack. We also perform experiments in the open-world scenario. When the parameter k of k-NN classifier is set to 5, then we can obtain a true positive rate of 90.96% with a false positive rate of 3.9%.