Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Boosting is a general method for improving the accuracy of learning algorithms.  use boosting to construct improved {\em privacy-preserving synopses} of an input database. These are data structures that yield, for a given set $\Q$ of queries over an input database, reasonably accurate estimates of the responses to every query in~$\Q$, even when the number of queries is much larger than the number of rows in the database. Given a {\em base synopsis generator} that takes a distribution on $\Q$ and produces a ``ak'' synopsis that yields ``good'' ansrs for a majority of the ight in $\Q$, our {\em Boosting for Queries} algorithm obtains a synopsis that is good for all of~$\Q$.  ensure privacy for the rows of the database, but the boosting is performed on the {\em queries}.  also provide the first synopsis generators for arbitrary sets of arbitrary low-sensitivity queries, {\it i.e.}, queries whose ansrs do not vary much under the addition or deletion of a single row. In the execution of our algorithm certain tasks, each incurring some privacy loss, are performed many times. To analyze the cumulative privacy loss,  obtain an $O(\eps^2)$ bound on the {\em expected} privacy loss from a single $\eps$-\dfp{} mechanism. Combining this with evolution of confidence arguments from the literature,  get stronger bounds on the expected cumulative privacy loss due to multiple mechanisms, each of which provides $\eps$-differential privacy or one of its relaxations, and each of which operates on (potentially) different, adaptively chosen, databases.
['Cynthia Dwork', 'Guy N. Rothblum', 'Salil P. Vadhan']
I am writing a research paper on a idea Boosting and Differential Privacy
[{"from": "human", "value": "I am writing a research paper on a idea Boosting and Differential Privacy"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Boosting is a general method for improving the accuracy of learning algorithms.  use boosting to construct improved {\\em privacy-preserving synopses} of an input database. These are data structures that yield, for a given set $\\Q$ of queries over an input database, reasonably accurate estimates of the responses to every query in~$\\Q$, even when the number of queries is much larger than the number of rows in the database. Given a {\\em base synopsis generator} that takes a distribution on $\\Q$ and produces a ``ak'' synopsis that yields ``good'' ansrs for a majority of the ight in $\\Q$, our {\\em Boosting for Queries} algorithm obtains a synopsis that is good for all of~$\\Q$.  ensure privacy for the rows of the database, but the boosting is performed on the {\\em queries}.  also provide the first synopsis generators for arbitrary sets of arbitrary low-sensitivity queries, {\\it i.e.}, queries whose ansrs do not vary much under the addition or deletion of a single row. In the execution of our algorithm certain tasks, each incurring some privacy loss, are performed many times. To analyze the cumulative privacy loss,  obtain an $O(\\eps^2)$ bound on the {\\em expected} privacy loss from a single $\\eps$-\\dfp{} mechanism. Combining this with evolution of confidence arguments from the literature,  get stronger bounds on the expected cumulative privacy loss due to multiple mechanisms, each of which provides $\\eps$-differential privacy or one of its relaxations, and each of which operates on (potentially) different, adaptively chosen, databases."}]