feasibility study of searchable image encryption system of ...psrcentre.org/images/extraimages/40....

Abstract—In this paper, we sketch the idea of searchable image

encryption system to provide the privacy and authentication on streaming service based on cloud computing. The searchable encryption system is the matrix of searchable image encryption system. By extending the streaming search from text search, the search of the streaming service is available, and supports personal privacy and authentication using DES encryption of JCE and CBIR technique.

Keywords— Searchable Image Encryption System, Streaming Service, Privacy.

I. INTRODUCTION IG data consists of datasets that grow so large that they become awkward to work with using on-hand database

management tools in information technology. Difficulties of big data include capture, storage, search, sharing, analytics, and visualizing. This trend continues because of the benefits of working with larger and larger datasets allowing analysts to "spot business trends, prevent diseases, and combat crime." Though a moving target, current limits are on the order of tera-bytes, exa-bytes and zetta-bytes of data. Scientists regularly encounter this problem in meteorology, genomics, connectomics, complex physics simulations, biological and environmental research, Internet search, finance, and business informatics. Data sets also grow in size because they are increasingly being gathered by ubiquitous information-sensing mobile devices, aerial sensory technologies (remote sensing), software logs, cameras, microphones, Radio-frequency identification readers, and wireless sensor networks. Every day, 2.5 quintillion bytes of data are created and 90% of the data in the world today was created within the past two years. Big data requires exceptional technologies to efficiently process large quantities of data within tolerable elapsed times. Technologies being applied to big data include massively parallel processing databases, data mining grids, distributed file systems,

JongGeun Jeong is with the Division of Electrical Engineering, Information Sciences & Convergence Research, National Research Foundation of Korea (NRF), South Korea (e-mail: [email protected]).

ByungRae Cha is with the School of Information and Communications, Gwangju Institute of Science and Technology, South Korea (e-mail: [email protected]).

Jongwon Kim is with the School of Information and Communications, Gwangju Institute of Science and Technology, South Korea (e-mail: [email protected]).

distributed databases, cloud computing platforms, the Internet, and scalable storage systems. Specially, big data contains the various streaming services and multimedia. In this paper, we propose the searchable image encryption system (SIES) on streaming media of cloud computing environment to provide the privacy and authentication [1]. In searchable encryption system (SES), the subject of information referred the document. That is, the document is the information users want to hide. Hence, the user provides information on a server to retrieve documents is called a keyword. In general, the data contained in the document as a set of keywords is defined. The searchable encryption systems of personal information stored in external storage space that occur as a workaround for the many problems have been studied until now. SES of the users' encryption keys can be classified into public key and private key [2~8].

II. DESIGN OF SEIS The proposed SIES has extended the streaming media and the

image keyword from the document and the keyword in cloud computing. That is, SIES has redefined instead of SES. It is able to search streaming media by image keyword from searching documentation. Additionally, the SIES can support the authentication and privacy of users as shown in Fig. 1.

Fig. 1 Technical concept of SIES

A. 1st Index and Extracted Image Keyword The SIES extracts the image keyword and 1st index in

Feasibility Study of Searchable Image Encryption System of Streaming Service based

on Cloud Computing Environment JongGeun Jeong, ByungRae Cha, and Jongwon Kim

B

International Conference on Data Mining and Computer Engineering (ICDMCE'2012) December 21-22, 2012 Bangkok (Thailand)

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steaming media by Content-based image retrieval (CBIR) technique as shown in Fig. 2. In Fig. 2, the poster cut is the collection of image keywords. The extracted images are called image keywords because each image in such an image array is referenced by one index or address of one part of streaming media. And the access control of poster cut needs user’s authentication.

Fig. 2 Process of 1st index and extracted image keyword

B. 1st and 2nd Key for Encryption and Decryption of Streaming Media

In pre-subsection, we describe the process of 1st index and image keyword extracted one part of streaming media. Fig. 3 shows the streaming media and extracted image keyword in one part of streaming media.

Fig. 3 (a) Streaming media, (b) Extracted image keyword in streaming media

Fig. 4 Encryption of streaming media and image keyword

Fig. 4 and Fig. 5 show the encryption and decryption process of streaming media by 1st key and 2nd key groups. And Fig. 8 shows the poster cut area for image keyword extraction on streaming media by CBIR technique. CBIR is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large

databases. "Content-based" means that the search will analyze the actual contents (refer to colors, shapes, textures, or any other information) of the image rather than the metadata such as keywords, tags, and/or descriptions associated with the image.

Fig. 5 Decryption of image keyword and one part of streaming media

C. Spatial Color Index for Image Kewordy Query The image pixels can be viewed as a data set having two

dimensions viz., color and location. Considering the color and location aspects of each pixel, an image can be characterized by a set of objects of interest referred to as color clusters of all sharps. The color clusters referred to as objects, are identified, it becomes easier for global or local similarity search of images. Here, a spatial color indexing scheme for CBIR is introduced which is designed based on a color clustering technique in a two dimensional plane as shown in Fig. 6. [9]

Fig. 6 Spatial color indexing scheme for image keyword query


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III. SIMULATIONS

A. Post Cut Division of Streaming Media In this section, we verify the possibility of SIES through

simulations of post cut area selection and encryption of streaming media, and simulate the process of post cut by CBIR technique in aspect of RGB. The sample streaming media has 720 frames in Window 7.

Fig. 7 Sample streaming media of 720 frame in Windows 7

Fig. 8 Area analysis of Post Cut on sample streaming

Fig. 8 presents 7 areas in result of the area analysis to post

cuts. In location of matrix (1, 1) of Fig. 8, it presents the distribution of red color. It shows 7 clusters of Red color. In location of matrix (1, 2) of Fig. 8, it presents the distribution of Green color. It shows 8 clusters of Blue color. In location of matrix (2, 1) of Fig. 8, it presents the distribution of red color. It shows 6 clusters of red color. In location of matrix (2, 2) of Fig. 8 lastly, it presents the distribution of mean of RGB colors. It shows 7 clusters of RGB color accurately. And Fig. 9 presents the first and last images in 7 areas of post cuts.

Fig. 9 First and last images in 7 areas of post cuts of sample streaming

B. Extraction of Image Key We simulate the extraction of image key in 64 frames of sin

signal. Fig. 10 (a) is 62 frames of streaming media, Fig. 10 (b) is color analysis process by CBIR Technique, and Fig. 10 (c) shows the image keyword by result of color analysis process.

Fig. 10 Image keyword extraction by CBIR technique


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C. CBIR using Spatial Color Indexing In this subsection, we simulate the basic step of spatial color

indexing using Gogh picture. Here, the image contour is the boundary line of similar color area around. Fig. 10 presents the original color image, grey image of original color image, and optimal grey image of original color image by spatial color indexing processing. It shows 4 parts division of grey image. Fig. 11 presents the divisions of various color areas in original image by spatial color indexing. The matrix (1, 1) of Fig. 11 presents only 2 parts of original color image by spatial color indexing. And the matrix (3, 3) of Fig. 11 shows 10 parts of original image by spatial color indexing. Fig. 12 presents the comparison of file generation time and size about image contour numbers by spatial color indexing.

Fig. 2 Spatial indexing of grey image

Fig. 3 Spatial indexing of color image

Fig. 4 Comparison of (a) file generation time and (b) file size about

image contour numbers of color image

D. Encryption and Decryption analysis of key image and streaming media

SIES primarily performs the encryption and decryption function by 1st and 2nd key to support the privacy. We simulate the encryption and decryption of image keyword and streaming media. And Table 1 shows encryption and decryption time of image keyword and streaming media on Intel Core 2 Duo 2.66GHz.

Table 1 Encryption and decryption time of image keyword and streaming media

Items Enc. Time(Sec) Dec. Time(Sec)

Image

48KB(180x257) 0.282 0.297

156KB(450x643) 0.297 0.297

192KB(900x1285) 0.312 0.312

Streaming

Media

4.5MB 0.625 0.61

963MB 68.703 64.672

1.423GB 101.391 100.109

IV. CONCLUSION In this paper, we proposed and simulated the searchable

image encryption system on streaming media based on cloud


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computing to support users’ authentication and privacy. The image keyword is generated by extraction and division of post cut in streaming media based on cloud computing environment. It performs keyword role of image search. And the spatial color indexing supports similar images search by image keyword query. In simulation, we can verify the feasibilities of SIES by the post cut of streaming media, image keyword extraction in post cut of streaming media, image contour by spatial color indexing, and encryption of image and streaming media.

REFERENCES [1] ByungRae Cha, NamHo Kim, JaeHyun Seo and JongWon Kim, “Idea

Sketch of Searchable Image Encryption System on Streaming Media,” SCTA 2012, Aug. 2012.

[2] N. S. Jho and D. W. Hong, “Technical Trend of the Searchable Encryption System,” ETRI Journal, Vol. 23, No. 4, Aug. 2008.

[3] P. Golle, J. Staddon, and B. Waters, “Secure Conjunctive Keyword Search over Encrypted Data,” In Applied Cryptography and Network Security Conference, 2004.

[4] B. Waters, D. Balfanz, G. Durfee, and D. Smetters, “Building an Encrypted and Searchable Auditlog,”NDSS, 2004.

[5] R. Ostrovsky and W. Skeith, “Private Searching on Streaming Data,” Crypto 2005.

[6] J. Bethencourt, H. Chan, A. Perrig, E. Shi, and D. Song, “Anonymous Multi-Attribute Encryption with Range Query Conditional Decryption,” Technical Report, C.M.U. 2006.

[7] R. Ostrovsky, “Software Protection and Simulations on Oblivious RAMs,” ACM Symp. on Theory of Computing, 1990.

[8] P. Golle and R. Ostrovsky, “Software Protection and Simulation on Oblivious RAMs,” Journal of ACM, Vol.43, No.3, 1996, pp.431-473.

[9] P J Dutta, D K Bhattacharyya, M Dutta, and J K Kalita, “Spatial Color Indexing Using Data Clustering Technique,” 2004.


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