Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  demonstrate that, ignoring computational constraints, it is possible to release privacy-preserving databases that are useful for all queries over a discretized domain from any given concept class with polynomial VC-dimension.  show a new lor bound for releasing databases that are useful for halfspace queries over a continuous domain. Despite this,  give a privacy-preserving polynomial time algorithm that releases information useful for all halfspace queries, for a slightly relaxed definition of usefulness. Inspired by learning theory,  introduce a new notion of data privacy, which  call distributional privacy, and show that it is strictly stronger than the prevailing privacy notion, differential privacy.
['Avrim Blum', 'Katrina Ligett', 'Aaron Roth']
I am writing a research paper on a idea A learning theory approach to non-interactive database privacy
[{"from": "human", "value": "I am writing a research paper on a idea A learning theory approach to non-interactive database privacy"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  demonstrate that, ignoring computational constraints, it is possible to release privacy-preserving databases that are useful for all queries over a discretized domain from any given concept class with polynomial VC-dimension.  show a new lor bound for releasing databases that are useful for halfspace queries over a continuous domain. Despite this,  give a privacy-preserving polynomial time algorithm that releases information useful for all halfspace queries, for a slightly relaxed definition of usefulness. Inspired by learning theory,  introduce a new notion of data privacy, which  call distributional privacy, and show that it is strictly stronger than the prevailing privacy notion, differential privacy."}]