Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Differential privacy provides the first theoretical foundation with provable privacy guarantee against adversaries with arbitrary prior knowledge. The main idea to achieve differential privacy is to inject random noise into statistical query results. Besides correctness, the most important goal in the design of a differentially private mechanism is to reduce the effect of random noise, ensuring that the noisy results can still be useful.    proposes the  compressive mechanism , a novel solution on the basis of state-of-the-art compression technique, called  compressive sensing . Compressive sensing is a decent theoretical tool for compact synopsis construction, using random projections. ,  show that the amount of noise is significantly reduced from  O ( n ) to  O (log( n )), when the noise insertion procedure is carried on the synopsis samples instead of the original database. As an extension,  also apply the proposed compressive mechanism to solve the problem of continual release of statistical results. Extensive experiments using real datasets justify our accuracy claims.
['Yang D. Li', 'Zhenjie Zhang', 'Marianne Winslett', 'Yin Yang']
I am writing a research paper on a idea Compressive mechanism: utilizing sparse representation in differential privacy
[{"from": "human", "value": "I am writing a research paper on a idea Compressive mechanism: utilizing sparse representation in 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: Differential privacy provides the first theoretical foundation with provable privacy guarantee against adversaries with arbitrary prior knowledge. The main idea to achieve differential privacy is to inject random noise into statistical query results. Besides correctness, the most important goal in the design of a differentially private mechanism is to reduce the effect of random noise, ensuring that the noisy results can still be useful.    proposes the  compressive mechanism , a novel solution on the basis of state-of-the-art compression technique, called  compressive sensing . Compressive sensing is a decent theoretical tool for compact synopsis construction, using random projections. ,  show that the amount of noise is significantly reduced from  O ( n ) to  O (log( n )), when the noise insertion procedure is carried on the synopsis samples instead of the original database. As an extension,  also apply the proposed compressive mechanism to solve the problem of continual release of statistical results. Extensive experiments using real datasets justify our accuracy claims."}]