Abstract
In advanced health care systems, patients’ medical data can be outsourced to cloud servers to enable remote healthcare service providers to access and analyze patients’ data from any location to provide better treatment. However, outsourcing sensitive medical data makes data owners, i.e., patients, concerned about their privacy because private companies run the cloud service and the data can be accessed by them. Therefore, it is important to encrypt the data in the form of documents before outsourcing them to the cloud in a way that enables a data user, i.e., a doctor, to search over these documents without allowing the cloud provider to learn any private information about patients. Several schemes have been proposed to enable search over encrypted medical cloud data to preserve patient privacy, but the existing schemes suffer from high communication/computation overhead because they are designed for a single-data-owner setting. Moreover, they are not secure against known-plaintext/background and linkability attacks and do not allow doctors to customize their search to avoid downloading irrelevant documents. In this paper, we develop an efficient search scheme over encrypted data for a multi-data-owner setting. To secure our scheme, the cloud server obtains noisy similarity scores and doctors de-noise them to download the most relevant documents. Our scheme enables doctors to prescribe search conditions to customize the search without revealing the conditions to the server. Our formal proof and analysis indicate that our scheme can preserve privacy and is secure against known-plaintext/background and linkability attacks, and the results of extensive experiments demonstrate the efficiency of our scheme compared to the existing works.
Highlights
D UE to the cloud computing capability of storing large scale databases [1], the patients’ medical data can be outsourced to cloud servers through high speed cellular network, e.g., 5G network and beyond [2], [3]
Vi,j = KeywordScore + ai,jbx,y, which gives the noisy similarity score that is equal to the similarity score of the keywords part in vectors Vi,j and Qx,y (KeywordScore) masked by the random number ai,jbx,y, where ai,j is added by the patient in the document index and bx,y is added by the doctor in the trapdoor
We have proposed, EPSM, an efficient and secure search scheme over encrypted medical cloud data in multi-data-owner setting
Summary
D UE to the cloud computing capability of storing large scale databases [1], the patients’ medical data can be outsourced to cloud servers through high speed cellular network, e.g., 5G network and beyond [2], [3]. To enable doctors to download documents of interest without revealing any information to the server, several schemes have been developed for searching over encrypted data [11]–[15]. S. Abdelfattah et al.: Efficient Search over Encrypted Medical Data with Known-Plaintext/Background Models and Unlinkability doctor encrypts a vector (called trapdoor) that contains the keywords of the documents he/she wants to download and sends it to the cloud server. To address the aforementioned limitations, we propose EPSM: an Efficient and Privacypreserving Search over Medical cloud data with known plaintext/background and unlinkability security. Our security analysis proves that EPSM is secure under known plaintext/background models, and the cloud server cannot link two trapdoors (or indices) that have the same keywords.
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