Security Analysis on Privacy-Preserving Cloud Aided Biometric Identification Schemes

2016 ◽  
pp. 446-453 ◽  
Author(s):  
Shiran Pan ◽  
Shen Yan ◽  
Wen-Tao Zhu
Keyword(s):  
Security Analysis ◽  
Privacy Preserving ◽  
Biometric Identification ◽  
Identification Schemes

scholarly journals An Efficient and Privacy-Preserving Biometric Identification Scheme Based on the FITing-Tree

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaopeng Yang ◽  
Hui Zhu ◽  
Songnian Zhang ◽  
Rongxing Lu ◽  
Xuesong Gao
Keyword(s):  
Homomorphic Encryption ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Biometric Identification ◽  
Large Dataset ◽  
Biometric Data ◽  
Communication Costs ◽  
Identification Scheme ◽  
Biometric Template ◽  
Cloud Servers

Biometric identification services have been applied to almost all aspects of life. However, how to securely and efficiently identify an individual in a huge biometric dataset is still very challenging. For one thing, biometric data is very sensitive and should be kept secure during the process of biometric identification. On the other hand, searching a biometric template in a large dataset can be very time-consuming, especially when some privacy-preserving measures are adopted. To address this problem, we propose an efficient and privacy-preserving biometric identification scheme based on the FITing-tree, iDistance, and a symmetric homomorphic encryption (SHE) scheme with two cloud servers. With our proposed scheme, the privacy of the user’s identification request and service provider’s dataset is guaranteed, while the computational costs of the cloud servers in searching the biometric dataset can be kept at an acceptable level. Detailed security analysis shows that the privacy of both the biometric dataset and biometric identification request is well protected during the identification service. In addition, we implement our proposed scheme and compare it to a previously reported M-Tree based privacy-preserving identification scheme in terms of computational and communication costs. Experimental results demonstrate that our proposed scheme is indeed efficient in terms of computational and communication costs while identifying a biometric template in a large dataset.

scholarly journals Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hua Dai ◽  
Hui Ren ◽  
Zhiye Chen ◽  
Geng Yang ◽  
Xun Yi
Keyword(s):  
Data Privacy ◽  
Logistic Map ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Service Recommendation ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Sorting Algorithms ◽  
Common Operation ◽  
Cloud Servers

Outsourcing data in clouds is adopted by more and more companies and individuals due to the profits from data sharing and parallel, elastic, and on-demand computing. However, it forces data owners to lose control of their own data, which causes privacy-preserving problems on sensitive data. Sorting is a common operation in many areas, such as machine learning, service recommendation, and data query. It is a challenge to implement privacy-preserving sorting over encrypted data without leaking privacy of sensitive data. In this paper, we propose privacy-preserving sorting algorithms which are on the basis of the logistic map. Secure comparable codes are constructed by logistic map functions, which can be utilized to compare the corresponding encrypted data items even without knowing their plaintext values. Data owners firstly encrypt their data and generate the corresponding comparable codes and then outsource them to clouds. Cloud servers are capable of sorting the outsourced encrypted data in accordance with their corresponding comparable codes by the proposed privacy-preserving sorting algorithms. Security analysis and experimental results show that the proposed algorithms can protect data privacy, while providing efficient sorting on encrypted data.

scholarly journals Study of privacy-preserving framework for cloud storage

2011 ◽  
Vol 8 (3) ◽  
pp. 801-819 ◽  
Author(s):  
Huang Ruwei ◽  
Gui Xiaolin ◽  
Yu Si ◽  
Zhuang Wei
Keyword(s):  
Data Storage ◽  
Cloud Storage ◽  
Security Analysis ◽  
Bloom Filter ◽  
Data Access ◽  
Data Retrieval ◽  
Privacy Preserving ◽  
Index Structure ◽  
Structure Generation ◽  
Access Right

In order to implement privacy-preserving, efficient and secure data storage and access environment of cloud storage, the following problems must be considered: data index structure, generation and management of keys, data retrieval, treatments of change of users? access right and dynamic operations on data, and interactions among participants. To solve those problems, the interactive protocol among participants is introduced, an extirpation-based key derivation algorithm (EKDA) is designed to manage the keys, a double hashed and weighted Bloom Filter (DWBF) is proposed to retrieve the encrypted keywords, which are combined with lazy revocation, multi-tree structure, asymmetric and symmetric encryptions, which form a privacypreserving, efficient and secure framework for cloud storage. The experiment and security analysis show that EKDA can reduce the communication and storage overheads efficiently, DWBF supports ciphertext retrieval and can reduce communication, storage and computation overhead as well, and the proposed framework is privacy preserving while supporting data access efficiently.

scholarly journals A privacy-preserving collaborative reputation system for mobile crowdsensing

2018 ◽  
Vol 14 (9) ◽  
pp. 155014771880218 ◽  
Author(s):  
Bayan Hashr Alamri ◽  
Muhammad Mostafa Monowar ◽  
Suhair Alshehri
Keyword(s):  
Security Analysis ◽  
Privacy Preserving ◽  
Reputation System ◽  
Mobile Crowdsensing ◽  
Sensing Applications ◽  
Conflicting Objectives ◽  
Trusted Data ◽  
Privacy Issues ◽  
Open Nature

Mobile crowdsensing is an emerging technology in which participants contribute sensor readings for different sensing applications. This technology enables a broad range of sensing applications by utilizing smartphones and tablets worldwide to improve people’s quality of life. Protecting participants’ privacy and ensuring the trustworthiness of the sensor readings are conflicting objectives and key challenges in this field. Privacy issues arise from the disclosure of the participant-related context information, such as participants’ location. Trustworthiness issues arise from the open nature of sensing system because anyone can contribute data. This article proposes a privacy-preserving collaborative reputation system that preserves privacy and ensures data trustworthiness of the sensor readings for mobile crowdsensing applications. The proposed work also counters a number of possible attacks that might occur in mobile crowdsensing applications. We provide a detailed security analysis to prove the effectiveness of privacy-preserving collaborative reputation system against a number of attacks. We conduct an extensive simulation to investigate the performance of our schema. The obtained results show that the proposed schema is practical; it succeeds in identifying malicious users in most scenarios. In addition, it tolerates a large number of colluding adversaries even if their number surpass 65%. Moreover, it detects on-off attackers even if they report trusted data with high probability (0.8).

scholarly journals An Efficient and Privacy-Preserving Multiuser Cloud-Based LBS Query Scheme

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Lu Ou ◽  
Hui Yin ◽  
Zheng Qin ◽  
Sheng Xiao ◽  
Guangyi Yang ◽  
...  
Keyword(s):  
Location Privacy ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Location Information ◽  
User Privacy ◽  
User Revocation ◽  
And Performance ◽  
Cloud Infrastructures

Location-based services (LBSs) are increasingly popular in today’s society. People reveal their location information to LBS providers to obtain personalized services such as map directions, restaurant recommendations, and taxi reservations. Usually, LBS providers offer user privacy protection statement to assure users that their private location information would not be given away. However, many LBSs run on third-party cloud infrastructures. It is challenging to guarantee user location privacy against curious cloud operators while still permitting users to query their own location information data. In this paper, we propose an efficient privacy-preserving cloud-based LBS query scheme for the multiuser setting. We encrypt LBS data and LBS queries with a hybrid encryption mechanism, which can efficiently implement privacy-preserving search over encrypted LBS data and is very suitable for the multiuser setting with secure and effective user enrollment and user revocation. This paper contains security analysis and performance experiments to demonstrate the privacy-preserving properties and efficiency of our proposed scheme.

scholarly journals Efficient Certificateless Conditional Privacy-Preserving Authentication Scheme in VANETs

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Yang Ming ◽  
Hongliang Cheng
Keyword(s):  
Traffic Safety ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Security Analysis ◽  
Real Life ◽  
Random Oracle Model ◽  
Random Oracle ◽  
Privacy Preserving ◽  
Bilinear Pairing ◽  
Security And Privacy

Vehicular ad hoc networks (VANETs) are an increasing important paradigm for greatly enhancing roadway system efficiency and traffic safety. To widely deploy VANETs in real life, it is critical to deal with the security and privacy issues in VANETs. In this paper, we propose a certificateless conditional privacy preserving authentication (CCPPA) scheme based on certificateless cryptography and elliptic curve cryptography for secure vehicle-to-infrastructure communication in VANETs. In the proposed scheme, a roadside unit (RSU) can simultaneously verify plenty of received messages such that the total verification time may be sharply decreased. Furthermore, the security analysis indicates that the proposed scheme is provably secure in the random oracle model and fulfills all the requirements on security and privacy. To further improve efficiency, both map-to-point hash operation and bilinear pairing operation are not employed. Compared with previous CCPPA schemes, the proposed scheme prominently cuts down computation delay of message signing and verification by 66.9%–85.5% and 91.8%–93.4%, respectively, and reduces communication cost by 44.4%. Extensive simulations show that the proposed scheme is practicable and achieves prominent performances of very little average message delay and average message loss ratio and thus is appropriate for realistic applications.

scholarly journals A Privacy-Preserving Traffic Monitoring Scheme via Vehicular Crowdsourcing

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1274 ◽  
Author(s):  
Chuan Zhang ◽  
Liehuang Zhu ◽  
Chang Xu ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Traffic Congestion ◽  
Road Safety ◽  
Security Analysis ◽  
Traffic Monitoring ◽  
Privacy Preserving ◽  
Average Speed ◽  
Traffic Conditions ◽  
Roadside Unit ◽  
Paillier Cryptosystem ◽  
Effectiveness And Efficiency

The explosive number of vehicles has given rise to a series of traffic problems, such as traffic congestion, road safety, and fuel waste. Collecting vehicles’ speed information is an effective way to monitor the traffic conditions and avoid vehicles’ congestion, however it may threaten vehicles’ location and trajectory privacy. Motivated by the fact that traffic monitoring does not need to know each individual vehicle’s speed and the average speed would be sufficient, we propose a privacy-preserving traffic monitoring (PPTM) scheme to aggregate vehicles’ speeds at different locations. In PPTM, the roadside unit (RSU) collects vehicles’ speed information at multiple road segments, and further cooperates with a service provider to calculate the average speed information for every road segment. To preserve vehicles’ privacy, both homomorphic Paillier cryptosystem and super-increasing sequence are adopted. A comprehensive security analysis indicates that the proposed PPTM can preserve vehicles’ identities, speeds, locations, and trajectories privacy from being disclosed. In addition, extensive simulations are conducted to validate the effectiveness and efficiency of the proposed PPTM scheme.

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