Efficient location privacy algorithm for Internet of Things (IoT) services and applications

Abstract

Location-based Services (LBS) have become a very important area for research with the rapid development of Internet of Things (IoT) technology and the ubiquitous use of smartphones and social networks in our daily lives. Although users can enjoy a lot of flexibility and conveniences from the LBS with IoT, they may also lose their privacy. Untrusted or malicious LBS servers with all users' information can track users in various ways or release personal data to third parties. In this work, we first analyze the current dummy-location selection (DLS) algorithm-an efficient location privacy preservation approach and design an attack algorithm for DLS (ADLS) for test emerging IoT security. For efficiently preserving user's location privacy, we propose a novel dummy location privacy-preserving (DLP) algorithm by considering both computational costs and various privacy requirements of different users. Extensive simulation experiments have been carried out to evaluate the efficiency of the proposed schemes. Evaluation results show that the ADLS algorithm has a high probability of identifying the user's real location out from chosen dummy locations in the DLS algorithm. Our proposed DLP algorithm has clear advantages over the DLS algorithm in term of lower probability of revealing the user's real location and improved computational cost and efficiency (i.e., time, speed, accuracy, and complexity) while preserve the same privacy level as DLS algorithm.

A Comprehensive Analysis on the Security Threats and their Countermeasures of IoT

“…Zero-Knowledge technique [65] would be the best solution for anonymity but it has a drawback that having a large processing power because of strong algorithm it cannot be implemented on the devices which have less consumption power. So K-anonymity is a best approach for less power physical devices in IoT network [66].…”

IoT Expunge

“…While data captured by sensors is useful for service provisioning, it has significant privacy implications. Several studies [9,12,40] have recently highlighted how sensor data can lead to unexpected inferences about individuals and their behavior. Regulations, such as General Data Protection Regulation (GDPR) [1], California Online Privacy Protection Act (CalOPPA) [2], and California Consumer Privacy Act (CCPA) [3], have imposed several requirements on the organizations in which they can retain their user data.…”

Privacy-preserving fusion of IoT and big data for e-health

“…As the data accumulated, the EHRs of the patients become medical big data with high volume, which faces a lot of challenges, such as the information privacy, search, updating and sharing. It is emergent to design a privacy-preserving e-health system for the fusion of IoT and medical big data [4,5,6], which handles these challenges.…”