A Cloud-Based Secure and Privacy-Preserving Clustering Analysis of Infectious Disease

Abstract

The early detection of where and when fatal infectious diseases outbreak is of critical importance to the public health. To effectively detect, analyze and then intervene the spread of diseases, people's health status along with their location information should be timely collected. However, the conventional practices are via surveys or field health workers, which are highly costly and pose serious privacy threats to participants. In this paper, we for the first time propose to exploit the ubiquitous cloud services to collect users' multi-dimensional data in a secure and privacy-preserving manner and to enable the analysis of infectious disease. Specifically, we target at the spatial clustering analysis using Kulldorf scan statistic and propose a key-oblivious inner product encryption (KOIPE) mechanism to ensure that the untrusted entity only obtains the statistic instead of individual's data. Furthermore, we design an anonymous and sybil-resilient approach to protect the data collection process from double registration attacks and meanwhile preserve participant's privacy against untrusted cloud servers. A rigorous and comprehensive security analysis is given to validate our design, and we also conduct extensive simulations based on real-life datasets to demonstrate the performance of our scheme in terms of communication and computing overhead.

Remote Surveillance System without Privacy Leakage for Contacts in Infectious Disease

“…Some privacy-preserving schemes in remote monitoring system for infectious disease have also been developed. Liu et al used a key-independent inner product encryption mechanism to ensure that untrusted entities can only obtain health statistics but not personal data, thereby realizing the protection of user's health and location data [19]. However, this work only achieved statistical analysis of health data, and the user's real location information was not obtained, so it cannot find new contacts.…”

“…(2) if user is legal (3) obtain the pseudo-ID from authority (4) else (5) reject the users' registration request and exit (6) write the pseudo-ID and PK hc into wireless sensor devices (7) for each health data at timestamp (8) data encryption by E hi (PK hc , (HI, pseu do − I D)) (9) data storage and pre-diagnosis analysis in health center (10) if HI is abnormal (11) send the related information to hospital by E ci (PK h , (C, HI, pseu do − I D)) (12) hospital sends the location request to the contact with its H i d (13) contact verifies the legality of the hospital by its identity ID (14) if H i d is legal (15) contact sends address to hospital by E a dd (PK h , (A dd ress, pseu do − I D)) (16) else (17) contact rejects the location request (18) end if (19) (1) for each location data LI collected at each timestamp (2) contact encrypts the location data by E li � E asym (PK hc , (pseu do − I D, E sym (k sym , LI))) (3) location database ←E li (4) end for (5) if contact is confirmed (6) hospital sends N_CD of the contact to the health center (7) using function E asym for the first layer, and function E sym is used for the second encryption and then uploaded to the health center for storage.…”