Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study a market for private data in which a data analyst publicly releases a statistic over a database of private information. Individuals that own the data incur a cost for their loss of privacy proportional to the differential privacy guarantee given by the analyst at the time of the release. The analyst incentivizes individuals by compensating them, giving rise to a privacy auction. Motivated by recommender systems, the statistic  consider is a linear predictor function with publicly known ights. The statistic can be vied as a prediction of the unknown data of a new individual, based on the data of individuals in the database.  formalize the trade-off beten privacy and accuracy in this setting, and show that a simple class of estimates achieves an order-optimal trade-off. It thus suffices to focus on auction mechanisms that output such estimates.  use this observation to design a truthful, individually rational, proportional-purchase mechanism under a fixed budget constraint.  show that our mechanism is 5-approximate in terms of accuracy compared to the optimal mechanism, and that no truthful mechanism can achieve a 2−e approximation, for any e>0.
['Pranav Dandekar', 'Nadia Fawaz', 'Stratis Ioannidis']
I am writing a research paper on a idea Privacy auctions for recommender systems
[{"from": "human", "value": "I am writing a research paper on a idea Privacy auctions for recommender systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study a market for private data in which a data analyst publicly releases a statistic over a database of private information. Individuals that own the data incur a cost for their loss of privacy proportional to the differential privacy guarantee given by the analyst at the time of the release. The analyst incentivizes individuals by compensating them, giving rise to a privacy auction. Motivated by recommender systems, the statistic  consider is a linear predictor function with publicly known ights. The statistic can be vied as a prediction of the unknown data of a new individual, based on the data of individuals in the database.  formalize the trade-off beten privacy and accuracy in this setting, and show that a simple class of estimates achieves an order-optimal trade-off. It thus suffices to focus on auction mechanisms that output such estimates.  use this observation to design a truthful, individually rational, proportional-purchase mechanism under a fixed budget constraint.  show that our mechanism is 5-approximate in terms of accuracy compared to the optimal mechanism, and that no truthful mechanism can achieve a 2\u2212e approximation, for any e>0."}]