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In this work, we demonstrate the design and implementation of a novel privacy-preserving blockchain for the resource-constrained Internet of Things (IoT). Blockchain, by design, ensures trust, provides built-in integrity of information and security of immutability in an IoT system without the need of a centralized entity. However, its slow transaction rate, lack of transaction privacy, and high resource consumption are three of the major hindrances to the practical realization of blockchain in IoT. While directed acyclic graphs (DAG)-based blockchain variants (e.g., hashgraph) improve the transaction rate, the other two problems remain open. To this end, we designed and constructed the prototype of a blockchain by utilizing the benefits of high transaction rate and miner-free transaction validation process from hashgraph. The proposed blockchain, coined as PrivLiteChain, implements the concept of local differential privacy to provide transaction privacy and temporal constraint to the lifecycle of the blockchain to make it lightweight. 

In Location-Based Services (LBS), users are required to disclose their precise location information to query a service provider. An untrusted service provider can abuse those queries to infer sensitive information on a user through spatio-temporal and historical data analyses. Depicting the drawbacks of existing privacy-preserving approaches in LBS, we propose a user-centric obfuscation approach, called KLAP, based on the three fundamental obfuscation requirements: k number of locations, l-diversity, and privacy area preservation. Considering user's sensitivity to different locations and utilizing Real-Time Traffic Information (RTTI), KLAP generates a convex Concealing Region (CR) to hide user's location such that the locations, forming the CR, resemble similar sensitivity and are resilient against a wide range of inferences in spatio-temporal domain. For the first time, a novel CR pruning technique is proposed to significantly improve the delay between successive CR submissions. We carry out an experiment with a real dataset to show its effectiveness for sporadic, frequent, and continuous service use cases.