Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A work on distributed, in-network aggregation assumes a benign population of participants. Unfortunately, modern distributed systems are plagued by malicious participants.   present a first step towards verifiable yet efficient distributed, in-network aggregation in adversarial settings.  describe a general framework and threat model for the problem and then present proof sketches, a compact verification mechanism that combines cryptographic signatures and Flajolet-Martin sketches to guarantee acceptable aggregation error bounds with high probability.  derive proof sketches for count aggregates and extend them for random sampling, which can be used to provide verifiable approximations for a broad class of data-analysis queries, e.g., quantiles and heavy hitters. Finally,  evaluate the practical use of proof sketches, and observe that adversaries can often be reduced to much smaller violations in practice than our worst-case bounds suggest.
['Minos N. Garofalakis', 'Joseph M. Hellerstein', 'Petros Maniatis']
I am writing a research paper on a idea Proof Sketches: Verifiable In-Network Aggregation
[{"from": "human", "value": "I am writing a research paper on a idea Proof Sketches: Verifiable In-Network Aggregation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A work on distributed, in-network aggregation assumes a benign population of participants. Unfortunately, modern distributed systems are plagued by malicious participants.   present a first step towards verifiable yet efficient distributed, in-network aggregation in adversarial settings.  describe a general framework and threat model for the problem and then present proof sketches, a compact verification mechanism that combines cryptographic signatures and Flajolet-Martin sketches to guarantee acceptable aggregation error bounds with high probability.  derive proof sketches for count aggregates and extend them for random sampling, which can be used to provide verifiable approximations for a broad class of data-analysis queries, e.g., quantiles and heavy hitters. Finally,  evaluate the practical use of proof sketches, and observe that adversaries can often be reduced to much smaller violations in practice than our worst-case bounds suggest."}]