Handling Scalability and Security in G-PBFT
G-PBFT introduces improvements in security to prevent Sybil attacks and handles scalability by managing node churn. It builds on the PBFT protocol with additional assumptions and terms for endorser nodes, using geographic timers and special transactions. However, the algorithm's effectiveness relies on the assumption that nodes cannot fake their location data, posing a potential limitation. Explore the proposed ideas, algorithm, and latency considerations to understand the approach better.
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Presentation Transcript
Claimed Contributions Improves security by preventing sybil attacks Scalable Handle Churn
Assumptions All normal PBFT assumptions Different nodes cannot report the same geographic information at the same time Malicious nodes can not fake location data: all nodes of an IoT-blockchain application are located in a small physical area, other nodes can identify the fake locations reported by a malicious nodes. For example, if there is no device in a specific position and geographic information reporting, it can be recognized as fake
Terms Endorser a node which participates in consensus Client a node which submits transactions Era a specific configuration of the network of endorsers Geographic Timer timer used to determine how long a node has remained stationary, also reset for an Endorser if it submits a block
Endorser There is a min & max for the number of Endorsers set in the genesis block Their geographic timer is used for block generation. A longer time in the geographic timer will have a higher chance of generating a new block. An IoT device that stays in a fixed location for a longer time represents a higher honesty, so they are given additional powers Can make special transactions which may: kick out an Endorser who moved suggest a client become an Endorser if it hasn t moved for 72 hours
Algorithm Bootstrap Normal PBFT During PBFT Endorsers can submit special transactions which change the configuration of Endorsers Every T seconds an Era switch is performed which handles this churn
Ideas The proposed algorithm handles scalability by treating clients as part of the network of nodes, as they do not participate in consensus this lowers the communication overhead Sybil attacks are handled by only allowing stationary nodes to participate in consensus It s an interesting idea but unrealistic to think nodes can t fake their location data and this assumption is key to the algorithm
