Efficient e-health data release with consistency guarantee under differential privacy

Abstract

E-health data release, which answers the statistical queries of the Electronic Health Records (EHRs), has been widely adopted in modern health care services. However, since the EHRs contain sensitive information of the patients, the data release procedure may lead to the leakage of the privacy of patients if it is done without necessary protection measures in place. On addressing this, existing research literature introduces differential privacy to provide the necessary privacy guarantee. However, it is not suitable in sensitive e-health environments because it lacks the efficiency for data processing and updating. In this paper, we propose an efficient e-health data release scheme with consistency guarantee under differential privacy. Specifically, we improve the performance of the previous work by designing a new private partition algorithm of histogram and also proposing a heuristic hierarchical query method. We conduct real experiments and compare our scheme with the existing one to show that the proposal is more efficient in terms of data processing and updating. Moreover, we increase the accuracy of data release through consistency and give proof of privacy to show that the proposed algorithm is under -differential privacy.

Differential privacy: its technological prescriptive using big data

“…Differential privacy has been termed as a promising technique that can achieve mathematically precise guarantee of privacy. The future work that could be explored is the other data release issues under DP, such as real time monitoring and publishing of e-health data [21]. 2.…”

“…Data can be released in the form of histogram, tree structure, time series, graph or pattern mining. So, one of the mechanisms presented here can be to apply DP on histogram data release [21]. The steps include:…”

“…The privacy of people in the training data is protected, but the privacy of people in the prediction data (that is, the data which will be applied to the trained model to) is not Design of adjustable privacy budget assignment strategies is required for better accuracy along with privacy guarantee e-Health data release [21] Improves the performance of the previous work by designing a new private partition algorithm of histogram and also proposing a heuristic hierarchical query method Real experiments were conducted and the schemes compared with the existing one to show that the proposal is more efficient in terms of data processing and updating Increase of the accuracy of data release through consistency and gives a proof of privacy to show that the proposed algorithm is under differential privacy Data release issues under differential privacy, such as real time monitoring and publishing of e-health data is proposed…”

“…Efficient e-health data release system model [21] Airavat model: security and privacy for Map Reduce [22] One more mechanism to give an end to end privacy and security guarantee can be to implement DP with access control mechanism such as mandatory access control (MAC) [22]. It basically consists of three components:…”

Privacy-Preserving mHealth Data Release with Pattern Consistency

“…DP has been applied to release the data of the histogram in Fig. 1 (left) [9], [10]. With DP, it can be guaranteed that an attacker cannot distinguish the original histogram and the histogram with any bin plus 1 or minus 1.…”