Statistical inference attack against PHY-layer key extraction and countermeasures

Empirical statistical inference attack against PHY-layer key extraction in real environments

MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM) ◽

10.1109/milcom.2017.8170755 ◽

2017 ◽

Author(s):

Rui Zhu ◽

Tao Shu ◽

Huirong Fu

Keyword(s):

Statistical Inference ◽

Inference Attack ◽

Phy Layer ◽

Key Extraction

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Cross-Platform Strong Privacy Protection Mechanism for Review Publication

Security and Communication Networks ◽

10.1155/2021/5556155 ◽

2021 ◽

Vol 2021 ◽

pp. 1-22

Author(s):

Mingzhen Li ◽

Yunfeng Wang ◽

Yang Xin ◽

Hongliang Zhu ◽

Qifeng Tang ◽

...

Keyword(s):

Statistical Inference ◽

Privacy Protection ◽

Service System ◽

Review System ◽

User Privacy ◽

Protection Mechanism ◽

Cross Platform ◽

Inference Attack

As a review system, the Crowd-Sourced Local Businesses Service System (CSLBSS) allows users to publicly publish reviews for businesses that include display name, avatar, and review content. While these reviews can maintain the business reputation and provide valuable references for others, the adversary also can legitimately obtain the user’s display name and a large number of historical reviews. For this problem, we show that the adversary can launch connecting user identities attack (CUIA) and statistical inference attack (SIA) to obtain user privacy by exploiting the acquired display names and historical reviews. However, the existing methods based on anonymity and suppressing reviews cannot resist these two attacks. Also, suppressing reviews may result in some reiews with the higher usefulness not being published. To solve these problems, we propose a cross-platform strong privacy protection mechanism (CSPPM) based on the partial publication and the complete anonymity mechanism. In CSPPM, based on the consistency between the user score and the business score, we propose a partial publication mechanism to publish reviews with the higher usefulness of review and filter false or untrue reviews. It ensures that our mechanism does not suppress reviews with the higher usefulness of reviews and improves system utility. We also propose a complete anonymity mechanism to anonymize the display name and avatars of reviews that are publicly published. It ensures that the adversary cannot obtain user privacy through CUIA and SIA. Finally, we evaluate CSPPM from both theoretical and experimental aspects. The results show that it can resist CUIA and SIA and improve system utility.

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