PriSN: a privacy protection framework for healthcare social networking sites

A new class of patient driven healthcare web applications are emerging to supplement and extend traditional healthcare delivery models and empower patient self-care. Patient-driven healthcare can be characterized as having an increased level of information flow, transparency, customization, collaboration, patient choice and responsibility-taking, as well as quantitative, predictive and preventive aspects. Social networking applications or sites are usually dedicated to fostering interaction between users. Healthcare Social Networking (HSN) sites constitute virtual communities where users connect with each other around common health issues and share relevant health data. HSNs have become very popular and broadly adopted by various medical professionals and patients. The growing use of HSNs has prompted public concerns about the underlying risks that such online data-sharing platforms pose to the privacy and security of Personal Health Information (PHI). This paper presents a set of privacy risks introduced by social networking applications in healthcare scenarios. The main contribution of this paper is the introduction of a privacy preserving framework, PriSN, which seeks to preserve the privacy of sensitive healthcare data of end-user in HSNs. PriSN safeguards a user's privacy by generalizing the contextual PHI collected in the HSN applications and shared with a given end-user's peers. To support multiple levels of granularity in the contextual PHI, the proposed obfuscation procedure establishes an ontological description stating the granularity of object instances.

[1]  Daqing Zhang,et al.  Protection of privacy in pervasive computing environments , 2005, International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II.

[2]  Katerina Gjorgjevska,et al.  Analysis of Healthcare Social Networking sites and applicability in Macedonian e-society , 2010, The 33rd International Convention MIPRO.

[3]  Oriana Riva,et al.  Challenges and Lessons in Developing Middleware on Smart Phones , 2008, Computer.

[4]  Frank Stajano,et al.  Location Privacy in Pervasive Computing , 2003, IEEE Pervasive Comput..

[5]  Claudio Bettini,et al.  Composition and Generalization of Context Data for Privacy Preservation , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

[6]  J. Frost,et al.  Sharing Health Data for Better Outcomes on PatientsLikeMe , 2010, Journal of medical Internet research.

[7]  Fabien L. Gandon,et al.  A Semantic E-Wallet to Reconcile Privacy and Context Awareness , 2003, SEMWEB.

[8]  Marco Gruteser,et al.  USENIX Association , 1992 .

[9]  Latanya Sweeney,et al.  Achieving k-Anonymity Privacy Protection Using Generalization and Suppression , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[10]  Walid G. Aref,et al.  Casper*: Query processing for location services without compromising privacy , 2006, TODS.

[11]  Sheikh Iqbal Ahamed,et al.  FPCS: A Formal Approach for Privacy-Aware Context-Based Services , 2008, 2008 32nd Annual IEEE International Computer Software and Applications Conference.

[12]  Jadwiga Indulska,et al.  Context Obfuscation for Privacy via Ontological Descriptions , 2005, LoCA.

[13]  Roy H. Campbell,et al.  Routing through the mist: privacy preserving communication in ubiquitous computing environments , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[14]  Claudio Bettini,et al.  Protecting Users' Anonymity in Pervasive Computing Environments , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

[15]  Marc Langheinrich,et al.  Privacy in Ubiquitous Computing , 2014 .

[16]  K. H. Kim,et al.  Emotion recognition system using short-term monitoring of physiological signals , 2004, Medical and Biological Engineering and Computing.