Practical quantum private query based on Bell state

2019 ◽  
Vol 34 (24) ◽  
pp. 1950196 ◽  
Author(s):  
Tao Zheng ◽  
Shibin Zhang ◽  
Xiang Gao ◽  
Yan Chang
Keyword(s):  
Privacy Protection ◽  
Photon Number ◽  
Bell State ◽  
Trojan Horse ◽  
User Privacy ◽  
Quantum Private Query ◽  
Secret Information ◽  
Malicious Behavior ◽  
Wavelength Filter ◽  
Trojan Horse Attack

Quantum private query (QPQ) is a cryptographic application that protects the privacy of both users and databases while querying the database secretly. In most existing QPQ protocols, the protection of user privacy can only be cheat-sensitive. Cheat-sensitive means that Bob will be found later with a certain probability if he tries to get the address queried by Alice. On the premise of cheat-sensitivity, although Alice can discover Bob’s malicious behavior after a query (transaction), the secret information of Alice was leaked in the completed query, which is likely to be a fatal blow to Alice. Or, to prevent Bob’s malicious behavior, Alice executes one or more additional queries to test Bob’s honesty. However, to bypass Alice’s honesty test, Bob can also provide several honest queries before performing dishonest queries. Therefore, cheat-sensitive should not be the ultimate goal of user privacy protection in QPQ. In this paper, we propose a practical QKD-based QPQ protocol with better user privacy protection than cheat-sensitivity based on order rearrangement of qubits. The proposed QPQ protocol can resist the Trojan horse attack even without wavelength filter and photon number splitter (PNS) equipped with auxiliary monitoring detectors.

A MATHEMATICAL TRUST MODEL TO ENSURE IN THE INFORMATION ENVIRONMENT

2020 ◽  
Author(s):  
Мадина Усенбай ◽  
Акмарал Иманбаева
Keyword(s):  
Trust Model ◽  
Time Parameter ◽  
Information Environment ◽  
User Privacy ◽  
Cloud Technology ◽  
Secret Information ◽  
Dynamic Objects

Конфиденциальность является одним из важных параметров для повышения безопасности в сети, цель которого - сохранить секретную информацию. Рассмотрена модель доверия, состоящая из текущих и прошлых оценок на основе репутации объекта в сети. В модели используется параметр времени для защиты конфиденциальности пользователя для статических и динамических объектов, например, в IoT или облачной технологии. Confidentiality is one of the important parameters for increasing security on the network, the coal of which is to keep secret information. A trust model consisting of current and past assessments based on the object reputation in the network is considered. The model uses a time parameter to protect user privacy for static and dynamic objects, for example, in IoT or cloud technology.

The Protection of User Preference Privacy in Personalized Information Retrieval: Challenges and Overviews

Libri ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Zongda Wu ◽  
Chenglang Lu ◽  
Youlin Zhao ◽  
Jian Xie ◽  
Dongdong Zou ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Privacy Protection ◽  
User Preference ◽  
User Privacy ◽  
Server Side ◽  
Algorithm Efficiency ◽  
Important Study ◽  
Basic Reference ◽  
Complete Set ◽  
Technical Features

Abstract This paper reviews a large number of research achievements relevant to user privacy protection in an untrusted network environment, and then analyzes and evaluates their application limitations in personalized information retrieval, to establish the conditional constraints that an effective approach for user preference privacy protection in personalized information retrieval should meet, thus providing a basic reference for the solution of this problem. First, based on the basic framework of a personalized information retrieval platform, we establish a complete set of constraints for user preference privacy protection in terms of security, usability, efficiency, and accuracy. Then, we comprehensively review the technical features for all kinds of popular methods for user privacy protection, and analyze their application limitations in personalized information retrieval, according to the constraints of preference privacy protection. The results show that personalized information retrieval has higher requirements for users’ privacy protection, i.e., it is required to comprehensively improve the security of users’ preference privacy on the untrusted server-side, under the precondition of not changing the platform, algorithm, efficiency, and accuracy of personalized information retrieval. However, all kinds of existing privacy methods still cannot meet the above requirements. This paper is an important study attempt to the problem of user preference privacy protection of personalized information retrieval, which can provide a basic reference and direction for the further study of the problem.

A dummy-based user privacy protection approach for text information retrieval

2020 ◽  
Vol 195 ◽  
pp. 105679
Author(s):  
Zongda Wu ◽  
Shigen Shen ◽  
Xinze Lian ◽  
Xinning Su ◽  
Enhong Chen
Keyword(s):  
Information Retrieval ◽  
Privacy Protection ◽  
User Privacy ◽  
Text Information ◽  
Text Information Retrieval

A qutrit quantum key distribution protocol using Bell inequalities with larger violation capabilities

2015 ◽  
Vol 15 (15&16) ◽  
pp. 1295-1306
Author(s):  
Zoe Amblard ◽  
Francois Arnault
Keyword(s):  
Quantum Key Distribution ◽  
Bell Inequality ◽  
Bell Inequalities ◽  
Key Distribution ◽  
Trojan Horse ◽  
Noise Resistance ◽  
The One ◽  
Trojan Horse Attack ◽  
Quantum Key Distribution Protocol

The Ekert quantum key distribution protocol [1] uses pairs of entangled qubits and performs checks based on a Bell inequality to detect eavesdropping. The 3DEB protocol [2] uses instead pairs of entangled qutrits to achieve better noise resistance than the Ekert protocol. It performs checks based on a Bell inequality for qutrits named CHSH-3 and found in [3, 4]. In this paper, we present a new protocol, which also uses pairs of entangled qutrits, but gaining advantage of a Bell inequality which achieves better noise resistance than the one used in 3DEB. The latter inequality is called here hCHSH-3 and was discovered in [5]. For each party, the hCHSH-3 inequality involves four observables already used in CHSH-3 but also two products of observables which do not commute. We explain how the parties can measure the observables corresponding to these products and thus are able to check the violation of hCHSH-3. In the presence of noise, this violation guarantees the security against a local Trojan horse attack. We also designed a version of our protocol which is secure against individual attacks.

The Unheard Story of Organizational Motivations Towards User Privacy

2020 ◽  
pp. 280-303
Author(s):  
Awanthika Senarath ◽  
Nalin Asanka Gamagedara Arachchilage
Keyword(s):  
Privacy Protection ◽  
End Users ◽  
User Privacy ◽  
Software Applications ◽  
Organizational Perspective ◽  
Scale Of Operation

There could be numerous reasons that drive organizations to provide privacy protections to end users in the applications they develop and maintain. Organizational motivations towards privacy affects the quality of privacy received by end users. Understanding these motivations and the approaches taken by organizations towards privacy protection would assist the policymakers and regulators to define effective frameworks encouraging organizational privacy practices. This study focuses on understanding the motivations behind organizational decisions and the approaches they take to embed privacy into the software applications. The authors analyzed 40 organizations different in size, scope, scale of operation, nature of data used, and revenue. they identified four groups of organizations characterized by the approach taken to provide privacy protection to their users. The taxonomy contributes to the organizational perspective of privacy. The knowledge presented here would help addressing the challenges in the domain of user privacy in software applications and services.

Privacy-Preserving Spatial Trajectory Prediction Based on a Novel Matrix Representation

2016 ◽  
pp. 1693-1717
Author(s):  
Wen-Chen Hu ◽  
Naima Kaabouch ◽  
Hung-Jen Yang ◽  
S. Hossein Mousavinezhad
Keyword(s):  
Traffic Control ◽  
Privacy Protection ◽  
Matrix Representation ◽  
High Mobility ◽  
Location Based Services ◽  
Privacy Concern ◽  
User Privacy ◽  
Trajectory Prediction ◽  
Spatial Trajectory ◽  
Human Behavior Recognition

Since the introduction of iPhone in 2007, smartphones have become very popular (e.g., the number of worldwide smartphone sales has surpassed the number of PC sales in 2011). The feature of high mobility and small size of smartphones has created many applications that are not possible or inconvenient for PCs and servers, even laptops. Location-based services (LBS), one of mobile applications, have attracted a great attention recently. This research proposes a location-based service, which predicts a spatial trajectory based on the current and previous trajectories by using a novel matrix representation. Spatial trajectory prediction can be used in a variety of purposes such as travel recommendations and traffic control and planning, but at the same time, just like most location-based services, the user privacy concern is a major issue. Without rigorous privacy protection, users would be reluctant to use the service. The proposed method is simple but effective and user privacy is rigorously preserved at the same time because the trajectory prediction is performed at the user-side. Additionally, this research is not only useful but also pedagogical because it involves a variety of topics like (i) mobile computing, (ii) mobile security, and (iii) human behavior recognition.

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