모바일 클라우드 스토리 서비스에서의 데이터 보안을 위한 데이터 접근 제어 및 보안 키 관리 기법

22kc

DFCloud: A TPM-based Secure Data Access Control Method of Cloud Storage in Mobile Devices

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.1113Nsciescopu

Neural Network Syntax Analyzer For Embedded Standardized Deep Learning

Deep learning frameworks based on the neural network model have attracted a lot of attention recently for their potential in various applications. Accordingly, recent developments in the fields of deep learning configuration platforms have led to renewed interests in neural network unified format (NNUF) for standardized deep learning computation. The attempt of making NNUF becomes quite challenging because primarily used platforms change over time and the structures of deep learning computation models are continuously evolving. This paper presents the design and implementation of a parser of NNUF for standardized deep learning computation. We call the platform implemented with the neural network exchange framework (NNEF) standard as the NNUF. This framework provides platform-independent processes for configuring and training deep learning neural networks, where the independence is offered by the NNUF model. This model allows us to configure all components of neural network graphs. Our framework also allows the resulting graph to be easily shared with other platform-dependent descriptions which configure various neural network architectures in their own ways. This paper presents the details of the parser design, JavaCC-based implementation, and initial results