scholarly journals DPLQ: Location‐based service privacy protection scheme based on differential privacy

2021 ◽  
Author(s):  
Qingyun Zhang ◽  
Xing Zhang ◽  
Mingyue Wang ◽  
Xiaohui Li
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Location Based Service ◽  
Protection Scheme

scholarly journals LBS privacy protection technology based on searchable encryption mechanism

2018 ◽  
Vol 189 ◽  
pp. 10013
Author(s):  
Tao Feng ◽  
Xudong Wang ◽  
Xinghua Li
Keyword(s):  
Global Positioning System ◽  
Privacy Protection ◽  
Location Privacy ◽  
Positioning System ◽  
Location Based Service ◽  
Text Search ◽  
Protection Scheme ◽  
Cipher Text ◽  
Global Positioning ◽  
Location Privacy Protection

Location based Service (the Location - -based Service, LBS) is a System is to transform the existing mobile communication network, wireless sensor networks, and Global Positioning System (Global Positioning System, GPS) with the combination of information Service mode, the general improvement in Positioning technology and the high popularity of mobile intelligent terminals, led to the growing market of LBS. This article from the perspective of LBS service privacy security, mainly studies the LBS location privacy protection scheme based on cipher text search, in LBS service location privacy and search information privacy issues, focus on to design the scheme, based on the cryptography in LBS service privacy protection issues in the process, this paper fully and secret cipher text search characteristics, design a new privacy protection of LBS service model, and expounds the system structure and working principle of model, defines the security properties of the privacy protection model and security model, Under the specific security assumptions, the new location privacy protection scheme based on lbspp-bse (LBS location privacy protection based on searchable encryption) is implemented.

A differential privacy protection scheme for sensitive big data in body sensor networks

2016 ◽  
Vol 71 (9-10) ◽  
pp. 465-475 ◽  
Author(s):  
Chi Lin ◽  
Pengyu Wang ◽  
Houbing Song ◽  
Yanhong Zhou ◽  
Qing Liu ◽  
...  
Keyword(s):  
Big Data ◽  
Sensor Networks ◽  
Privacy Protection ◽  
Differential Privacy ◽  
Body Sensor Networks ◽  
Protection Scheme

scholarly journals Differential Privacy Preserving in Big data Analytics for Body Area Networks

2021 ◽  
Vol 9 (12) ◽  
pp. 514-518
Author(s):  
Adam Gowri Shankar
Keyword(s):  
Big Data ◽  
Privacy Protection ◽  
Differential Privacy ◽  
Wearable Sensors ◽  
Big Data Analytics ◽  
Body Area Networks ◽  
Area Network ◽  
Sensitive Information ◽  
Body Area ◽  
Protection Scheme

Abstract: Body Area Networks (BANs), collects enormous data by wearable sensors which contain sensitive information such as physical condition, location information, and so on, which needs protection. Preservation of privacy in big data has emerged as an absolute prerequisite for exchanging private data in terms of data analysis, validation, and publishing. Previous methods and traditional methods like k-anonymity and other anonymization techniques have overlooked privacy protection issues resulting to privacy infringement. In this work, a differential privacy protection scheme for ‘big data in body area network’ is developed. Compared with previous methods, the proposed privacy protection scheme is best in terms of availability and reliability. Exploratory results demonstrate that, even when the attacker has full background knowledge, the proposed scheme can still provide enough interference to big sensitive data so as to preserve the privacy. Keywords: BAN’s, Privacy, Differential Privacy, Noisy response

scholarly journals Differential Privacy Location Protection Scheme Based on Hilbert Curve

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jie Wang ◽  
Feng Wang ◽  
Hongtao Li
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
Differential Privacy ◽  
Dimensional Space ◽  
Location Based Services ◽  
Data Availability ◽  
Location Information ◽  
Hilbert Curve ◽  
One Dimensional ◽  
Protection Scheme

Location-based services (LBS) applications provide convenience for people’s life and work, but the collection of location information may expose users’ privacy. Since these collected data contain much private information about users, a privacy protection scheme for location information is an impending need. In this paper, a protection scheme DPL-Hc is proposed. Firstly, the users’ location on the map is mapped into one-dimensional space by using Hilbert curve mapping technology. Then, the Laplace noise is added to the location information of one-dimensional space for perturbation, which considers more than 70% of the nonlocation information of users; meanwhile, the disturbance effect is achieved by adding noise. Finally, the disturbed location is submitted to the service provider as the users’ real location to protect the users’ location privacy. Theoretical analysis and simulation results show that the proposed scheme can protect the users’ location privacy without the trusted third party effectively. It has advantages in data availability, the degree of privacy protection, and the generation time of anonymous data sets, basically achieving the balance between privacy protection and service quality.

scholarly journals A Movie Recommendation System Based on Differential Privacy Protection

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Min Li ◽  
Yingming Zeng ◽  
Yue Guo ◽  
Yun Guo
Keyword(s):  
Privacy Protection ◽  
Recommendation System ◽  
Differential Privacy ◽  
Recommendation Systems ◽  
Current Recommendation ◽  
Protection Scheme ◽  
The Past ◽  
Collaborative Filtering Algorithm ◽  
Movie Recommendation ◽  
Privacy Budget

In the past decades, the ever-increasing popularity of the Internet has led to an explosive growth of information, which has consequently led to the emergence of recommendation systems. A series of cloud-based encryption measures have been adopted in the current recommendation systems to protect users’ privacy. However, there are still many other privacy attacks on the local devices. Therefore, this paper studies the encryption interference of applying a differential privacy protection scheme on the data in the user’s local devices under the assumption of an untrusted server. A dynamic privacy budget allocation method is proposed based on a localized differential privacy protection scheme while taking the specific application scene of movie recommendation into consideration. What is more, an improved user-based collaborative filtering algorithm, which adopts a matrix-based similarity calculation method instead of the traditional vector-based method when computing the user similarity, is proposed. Finally, it was proved by experimental results that the differential privacy-based movie recommendation system (DP-MRE) proposed in this paper could not only protect the privacy of users but also ensure the accuracy of recommendations.

scholarly journals Noise-added selection method for location-based service using differential privacy in Internet of Things

2019 ◽  
Vol 11 (1) ◽  
pp. 168781401882239 ◽  
Author(s):  
Zhimin Li ◽  
Haoze Lv ◽  
Zhaobin Liu
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Privacy Protection ◽  
Location Privacy ◽  
Differential Privacy ◽  
Poor Performance ◽  
Data Availability ◽  
Location Information ◽  
Location Based Service ◽  
Protection Method

With the development of Internet of Things, many applications need to use people’s location information, resulting in a large amount of data need to be processed, called big data. In recent years, people propose many methods to protect privacy in the location-based service aspect. However, existing technologies have poor performance in big data area. For instance, sensor equipments such as smart phones with location record function may submit location information anytime and anywhere which may lead to privacy disclosure. Attackers can leverage huge data to achieve useful information. In this article, we propose noise-added selection algorithm, a location privacy protection method that satisfies differential privacy to prevent the data from privacy disclosure by attacker with arbitrary background knowledge. In view of Internet of Things, we maximize the availability of data and algorithm when protecting the information. In detail, we filter real-time location distribution information, use our selection mechanism for comparison and analysis to determine privacy-protected regions, and then perform differential privacy on them. As shown in the theoretical analysis and the experimental results, the proposed method can achieve significant improvements in security, privacy, and complete a perfect balance between privacy protection level and data availability.

A user collaboration privacy protection scheme with threshold scheme and smart contract

2021 ◽  
Vol 560 ◽  
pp. 183-201
Author(s):  
Lei Zhang ◽  
Desheng Liu ◽  
Meina Chen ◽  
Hongyan Li ◽  
Chao Wang ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Threshold Scheme ◽  
Protection Scheme ◽  
Smart Contract ◽  
User Collaboration
Sign in / Sign up

Export Citation Format

Share Document