Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the context of statistical databases, the release of accurate statistical information about the collected data often puts at risk the privacy of the individual contributors. The goal of differential privacy is to maximise the utility of a query while protecting the individual records in the database. A natural way to achieve differential privacy is to add statistical noise to the result of the query.#R##N##R##N#In this context, a mechanism for releasing statistical information is thus a trade-off beten utility and privacy. In order to balance these two "conflicting" requirements, privacy preserving mechanisms calibrate the added noise to the so-called sensitivity of the query, and thus a precise estimate of the sensitivity of the query is necessary to determine the amplitude of the noise to be added.#R##N##R##N#,  initiate a systematic study of sensitivity of counting queries over relational databases.  first observe that the sensitivity of a Relational Algebra query with counting is not computable in general, and that while the sensitivity of Conjunctive Queries with counting is computable, it becomes unbounded as soon as the query includes a join.  then consider restricted classes of databases (databases with constraints), and study the problem of computing the sensitivity of a query given such constraints.  are able to establish bounds on the sensitivity of counting conjunctive queries over constrained databases. The kind of constraints studied here are: functional dependencies and cardinality dependencies. The latter is a natural generalisation of functional dependencies that allows us to provide tight bounds on the sensitivity of counting conjunctive queries.
['Myrto Arapinis', 'Diego Figueira', 'Marco Gaboardi']
I am writing a research paper on a idea Sensitivity of Counting Queries
[{"from": "human", "value": "I am writing a research paper on a idea Sensitivity of Counting Queries"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the context of statistical databases, the release of accurate statistical information about the collected data often puts at risk the privacy of the individual contributors. The goal of differential privacy is to maximise the utility of a query while protecting the individual records in the database. A natural way to achieve differential privacy is to add statistical noise to the result of the query.#R##N##R##N#In this context, a mechanism for releasing statistical information is thus a trade-off beten utility and privacy. In order to balance these two \"conflicting\" requirements, privacy preserving mechanisms calibrate the added noise to the so-called sensitivity of the query, and thus a precise estimate of the sensitivity of the query is necessary to determine the amplitude of the noise to be added.#R##N##R##N#,  initiate a systematic study of sensitivity of counting queries over relational databases.  first observe that the sensitivity of a Relational Algebra query with counting is not computable in general, and that while the sensitivity of Conjunctive Queries with counting is computable, it becomes unbounded as soon as the query includes a join.  then consider restricted classes of databases (databases with constraints), and study the problem of computing the sensitivity of a query given such constraints.  are able to establish bounds on the sensitivity of counting conjunctive queries over constrained databases. The kind of constraints studied here are: functional dependencies and cardinality dependencies. The latter is a natural generalisation of functional dependencies that allows us to provide tight bounds on the sensitivity of counting conjunctive queries."}]