CPP : A content‐aware privacy protection method for location‐based service

2021 ◽  
Author(s):  
Jiabang Liu ◽  
Xutong Jiang ◽  
Song Zhang ◽  
Bowen Liu ◽  
Wanchun Dou
Keyword(s):  
Privacy Protection ◽  
Location Based Service ◽  
Protection Method ◽  
Content Aware

scholarly journals Unchained Cellular Obfuscation Areas for Location Privacy in Continuous Location-Based Service Queries

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Jia-Ning Luo ◽  
Ming-Hour Yang
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
Personal Information ◽  
Background Information ◽  
Continuous Location ◽  
Location Based Service ◽  
Points Of Interest ◽  
Protection Method ◽  
Query Logs ◽  
Road Maps

To access location-based service (LBS) and query surrounding points of interest (POIs), smartphone users typically use built-in positioning functions of their phones when traveling at unfamiliar places. However, when a query is submitted, personal information may be leaked when they provide their real location. Current LBS privacy protection schemes fail to simultaneously consider real map conditions and continuous querying, and they cannot guarantee privacy protection when the obfuscation algorithm is known. To provide users with secure and effective LBSs, we developed an unchained regional privacy protection method that combines query logs and chained cellular obfuscation areas. It adopts a multiuser anonymizer architecture to prevent attackers from predicting user travel routes by using background information derived from maps (e.g., traffic speed limits). The proposed scheme is completely transparent to users when performing continuous location-based queries, and it combines the method with actual road maps to generate unchained obfuscation areas that conceal the actual locations of users. In addition to using a caching approach to enhance performance, the proposed scheme also considers popular tourist POIs to enhance the cache data hit ratio and query performance.

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.

scholarly journals Privacy Protection Scheme for Data Aggregation in Wireless Sensor Networks Based on PRDA+ Protocol

2018 ◽  
Vol 14 (11) ◽  
pp. 40
Author(s):  
Bohua Guo ◽  
Yanwu Zhang
Keyword(s):  
Data Aggregation ◽  
Privacy Protection ◽  
Random Number ◽  
Polynomial Regression ◽  
Homomorphic Encryption ◽  
Weighted Average ◽  
Base Station ◽  
Wireless Sensor ◽  
Protection Scheme ◽  
Protection Method

<p class="0abstract"><span lang="EN-US">To improve the data aggregation privacy protection scheme in wireless sensor network (WSN), a new scheme is put forward based on the privacy protection of polynomial regression and the privacy protection method based on the homomorphic encryption. The polynomial data aggregation (PRDA+) protocol is also proposed. In this scheme, the node and the base station will pre-deploy a secret key, and the random number generator encrypts the random number for the seed through the private key, which protects the privacy of the data. Then, by comparing the decrypted aggregate data through the correlation between the two metadata, the integrity protection of the data is realized. A weighted average aggregation scheme that can be verified is proposed. In view of the different importance of user information, the corresponding weights are set for each sensor node. EL Gamal digital signature is used to authenticate sensor nodes. The results show that the signature verification algorithm enables the scheme to resist data tampering and data denial, and to trace the source of erroneous data.</span></p>

Blockchain-based Data Integrity and Privacy Protection Method for Swarm Robotics

2021 ◽  
pp. 698-707
Author(s):  
Bangyin Li ◽  
Yutao Chen ◽  
Zhiqiang Zuo ◽  
Jie Huang
Keyword(s):  
Privacy Protection ◽  
Swarm Robotics ◽  
Data Integrity ◽  
Protection Method
Sign in / Sign up

Export Citation Format

Share Document