Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: To enjoy the advantages of cloud service while preserving security and privacy, huge data are increasingly outsourced to cloud in encrypted form. Unfortunately, most conventional encryption schemes cannot smoothly support encrypted data analysis and processing. As a significant topic, several schemes have been recently proposed to securely compute kk-nearest neighbors (kk-NN) on encrypted data being outsourced to cloud server (CS). Hover, most existing kk-NN search methods assume query users (QUs) are fully-trusted and know the key of data owner (DO) to encrypt/decrypt outsourced database. It is not realistic in many situations.#R##N##R##N#,  propose a new secure kk-NN query scheme on encrypted cloud data. Our approach simultaneously achieves: (1) data privacy against CS: the encrypted database can resist potential attacks of CS, (2) key confidentiality against QUs: to avoid the problems caused by key-sharing, QUs cannot learn DO’s key, (3) query privacy against CS and DO: the privacy of query points is preserved as ll, (4) query controllability  : QUs cannot launch a feasible kk-NN query for any new point without approval of DO.  provide theoretical guarantees for security and privacy properties, and show the efficiency of our scheme through extensive experiments.
['Youwen Zhu', 'Zhiqiu Huang', 'Tsuyoshi Takagi']
I am writing a research paper on a idea Secure and controllable k-NN query over encrypted cloud data with key confidentiality
[{"from": "human", "value": "I am writing a research paper on a idea Secure and controllable k-NN query over encrypted cloud data with key confidentiality"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: To enjoy the advantages of cloud service while preserving security and privacy, huge data are increasingly outsourced to cloud in encrypted form. Unfortunately, most conventional encryption schemes cannot smoothly support encrypted data analysis and processing. As a significant topic, several schemes have been recently proposed to securely compute kk-nearest neighbors (kk-NN) on encrypted data being outsourced to cloud server (CS). Hover, most existing kk-NN search methods assume query users (QUs) are fully-trusted and know the key of data owner (DO) to encrypt/decrypt outsourced database. It is not realistic in many situations.#R##N##R##N#,  propose a new secure kk-NN query scheme on encrypted cloud data. Our approach simultaneously achieves: (1) data privacy against CS: the encrypted database can resist potential attacks of CS, (2) key confidentiality against QUs: to avoid the problems caused by key-sharing, QUs cannot learn DO\u2019s key, (3) query privacy against CS and DO: the privacy of query points is preserved as ll, (4) query controllability \u00a0: QUs cannot launch a feasible kk-NN query for any new point without approval of DO.  provide theoretical guarantees for security and privacy properties, and show the efficiency of our scheme through extensive experiments."}]