Efficient Privacy-Preserving Outsourcing of Large-Scale Geometric Programming

2018 ◽  
Author(s):  
Wei Bao ◽  
Qinghua Li
Keyword(s):  
Geometric Programming ◽  
Large Scale ◽  
Privacy Preserving

Secure and Privacy Preserving Keyword Search over the Large Scale Cloud Data

2016 ◽  
pp. 200-215
Author(s):  
Wei Zhang ◽  
Jie Wu ◽  
Yaping Lin
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Keyword Search ◽  
Data Encryption ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Data Utilization ◽  
Cloud Data ◽  
Efficient Resource ◽  
Efficient Data

Cloud computing has attracted a lot of interests from both the academics and the industries, since it provides efficient resource management, economical cost, and fast deployment. However, concerns on security and privacy become the main obstacle for the large scale application of cloud computing. Encryption would be an alternative way to relief the concern. However, data encryption makes efficient data utilization a challenging problem. To address this problem, secure and privacy preserving keyword search over large scale cloud data is proposed and widely developed. In this paper, we make a thorough survey on the secure and privacy preserving keyword search over large scale cloud data. We investigate existing research arts category by category, where the category is classified according to the search functionality. In each category, we first elaborate on the key idea of existing research works, then we conclude some open and interesting problems.

scholarly journals An Efficient Identity-Based Conditional Privacy-Preserving Authentication Scheme for Secure Communication in a Vehicular Ad Hoc Network

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1687 ◽  
Author(s):  
Mahmood A. Al-shareeda ◽  
Mohammed Anbar ◽  
Selvakumar Manickam ◽  
Iznan H. Hasbullah
Keyword(s):  
Secure Communication ◽  
Large Scale ◽  
Ad Hoc ◽  
Privacy Preserving ◽  
Authentication Scheme ◽  
Security And Privacy ◽  
Group Signature ◽  
Side Channel ◽  
Sensitive Information ◽  
Identity Based

The security and privacy issues in vehicular ad hoc networks (VANETs) are often addressed with schemes based on either public key infrastructure, group signature, or identity. However, none of these schemes appropriately address the efficient verification of multiple VANET messages in high-density traffic areas. Attackers could obtain sensitive information kept in a tamper-proof device (TPD) by using a side-channel attack. In this paper, we propose an identity-based conditional privacy-preserving authentication scheme that supports a batch verification process for the simultaneous verification of multiple messages by each node. Furthermore, to thwart side-channel attacks, vehicle information in the TPD is periodically and frequently updated. Finally, since the proposed scheme does not utilize the bilinear pairing operation or the Map-To-Point hash function, its performance outperforms other schemes, making it viable for large-scale VANETs deployment.

Privacy-preserving categorization of mobile applications based on large-scale usage data

2020 ◽  
Vol 514 ◽  
pp. 557-570
Author(s):  
Yongzhong He ◽  
Chao Wang ◽  
Guangquan Xu ◽  
Wenjuan Lian ◽  
Hequn Xian ◽  
...  
Keyword(s):  
Mobile Applications ◽  
Large Scale ◽  
Privacy Preserving ◽  
Scale Usage ◽  
Usage Data

scholarly journals Towards Large-Scale and Privacy-Preserving Contact Tracing in COVID-19 pandemic: A Blockchain Perspective

2020 ◽  
pp. 1-1
Author(s):  
Wenzhe Lv ◽  
Sheng Wu ◽  
Chunxiao Jiang ◽  
Yuanhao Cui ◽  
Xuesong Qiu ◽  
...  
Keyword(s):  
Large Scale ◽  
Privacy Preserving ◽  
Contact Tracing

Accurate and large-scale privacy-preserving data mining using the election paradigm

2009 ◽  
Vol 68 (11) ◽  
pp. 1224-1236 ◽  
Author(s):  
Emmanouil Magkos ◽  
Manolis Maragoudakis ◽  
Vassilis Chrissikopoulos ◽  
Stefanos Gritzalis
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Privacy Preserving ◽  
Privacy Preserving Data Mining

scholarly journals Secure Privacy Preserving Record Linkage of Large Databases by Modified Bloom Filter Encodings

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Rainer Schnell ◽  
Christian Borgs
Keyword(s):  
Record Linkage ◽  
Large Scale ◽  
Bloom Filter ◽  
Privacy Preserving ◽  
Error Rates ◽  
Bloom Filters ◽  
Data Sets ◽  
Research Subjects ◽  
Practical Applications ◽  
Large Databases

ABSTRACTObjectiveIn most European settings, record linkage across different institutions has to be based on personal identifiers such as names, birthday or place of birth. To protect the privacy of research subjects, the identifiers have to be encrypted. In practice, these identifiers show error rates up to 20% per identifier, therefore linking on encrypted identifiers usually implies the loss of large subsets of the databases. In many applications, this loss of cases is related to variables of interest for the subject matter of the study. Therefore, this kind of record-linkage will generate biased estimates. These problems gave rise to techniques of Privacy Preserving Record Linkage (PPRL). Many different PPRL techniques have been suggested within the last 10 years, very few of them are suitable for practical applications with large database containing millions of records as they are typical for administrative or medical databases. One proven technique for PPRL for large scale applications is PPRL based on Bloom filters.MethodUsing appropriate parameter settings, Bloom filter approaches show linkage results comparable to linkage based on unencrypted identifiers. Furthermore, this approach has been used in real-world settings with data sets containing up to 100 Million records. By the application of suitable blocking strategies, linking can be done in reasonable time.ResultHowever, Bloom filters have been subject of cryptographic attacks. Previous research has shown that the straight application of Bloom filters has a nonzero re-identification risk. We will present new results on recently developed techniques to defy all known attacks on PPRL Bloom filters. These computationally simple algorithms modify the identifiers by different cryptographic diffusion techniques. The presentation will demonstrate these new algorithms and show their performance concerning precision, recall and re-identification risk on large databases.

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