Using the cloud storage services, users can access their data in any time, at any place, even with any computing device including mobile devices. Although these properties provide flexibility and scalability in handling data, security issues should be handled especially when mobile devices try to access data stored in cloud storage. Currently, a typical cloud storage service, Dropbox, offers server-side data encryption for security purpose. However, we think such method is not secure enough because all the encryption keys are managed by software and there is no attestation on the client software integrity. Moreover, a simple user identification based on user ID and Password is also easy to be compromised. Data sharing which is critical in enterprise environment is significantly restricted because it is not easy to share encryption key among users. In this paper, we propose DFCloud, a secure data access control method of cloud storage services to handle these problems found in the typical cloud storage service Dropbox. DFCloud relies on Trusted Platform Module (TPM) [1] to manage all the encryption keys and define a key sharing protocol among legal users. We assume that each client is mobile device using ARM TrustZone [2] technology. The DFCloud server prototype is implemented using ARM Fastmodel 7.1 and Open Virtualization software stack for ARM TrustZone. For DFCloud client, TPM functions are developed in the secure domain of ARM TrustZone because most ARM-based mobile devices are not equipped with TPM chip. The DFCloud framework defines TPM-based secure channel setup, TPM-based key management, remote client attestation, and a secure key share protocol across multiple users/devices. It is shown that our concept works correctly through a prototype implementation.

MOTIVATION: Systems biology attempts to describe complex systems behaviors in terms of dynamic operations of biological networks. However, there is lack of tools that can effectively decode complex network dynamics over multiple conditions. RESULTS: We present principal network analysis (PNA) that can automatically capture major dynamic activation patterns over multiple conditions and then generate protein and metabolic subnetworks for the captured patterns. We first demonstrated the utility of this method by applying it to a synthetic dataset. The results showed that PNA correctly captured the subnetworks representing dynamics in the data. We further applied PNA to two time-course gene expression profiles collected from (i) MCF7 cells after treatments of HRG at multiple doses and (ii) brain samples of four strains of mice infected with two prion strains. The resulting subnetworks and their interactions revealed network dynamics associated with HRG dose-dependent regulation of cell proliferation and differentiation and early PrPSc accumulation during prion infection. AVAILABILITY: The web-based software is available at: http://sbm.postech.ac.kr/pna.