On compressing encrypted data

By Mark Johnson, Prakash Ishwar, Vinod M. Prabhakaran, D. Schonberg, Kannan Ramchandran. IEEE Transactions on Signal Processing, 2004

434 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/372d0daeb0b227ebcbc4d2dbdf818052ab383752

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Xiao Zeng
2. Xinpeng Zhang
3. Nenghai Yu
4. Ming Chen
5. Lixin Luo
6. Kede Ma
7. Zhenyong Chen
8. Zhang Xiong

In Computer Vision and Pattern Recognition

1. Xinpeng Zhang
2. Weiming Zhang
3. Zhenxing Qian
4. Nenghai Yu
5. Kede Ma
6. Xiaojie Guo
7. Xiaochun Cao
8. Xingxing Wei

In Electrical and Electronic Engineering

1. Nenghai Yu
2. Weiming Zhang
3. Kede Ma
4. Xinpeng Zhang
5. Wenjun Kevin Zeng
6. Qiuming Yao
7. Lina Dong
8. Zhenxing Qian

Citations in the corpus

• Efficient Compression of Encrypted Grayscale Images (2010, methodology): “prove in [1] that in this case, if stream cipher is used by Alice, and Bob holds the secret key and performs joint decryption and decompression, the overall system performance can be as good as the conventional approach.”
• High Capacity Reversible Data Hiding in Encrypted Images by Patch-Level Sparse Representation (2016, background): “Thus the schemes in [23]–[27] can only achieve small payloads even with the advance of compressing encrypted images [31], [32].”
• Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption (2013, methodology): “To separate the data extraction from image decryption, Zhang [18] emptied out space for data embedding following the idea of compressing encrypted images [14], [15].”
• Reversible Image Watermarking Using Interpolation Technique (2010, methodology): “Finally, we can restore the original pixels through Compared with previous DE [3]–[7], the additive interpolation-error expansion is advantageous in three aspects: first, the distortion of additive expansion is smaller since each pixel is altered at most by 1.”
• Separable Reversible Data Hiding in Encrypted Image (2012, background): “…though he does not know the original image content. some parameters are embedded into a small number of encrypted pixels, and the of the other encrypted pixels are compressed to create a space for accommodating the additional data and the original data at the positions occupied by the parameters.”
• Reversibility improved data hiding in encrypted images (2014, methodology): “To enable data embedding in the encrypted image, all the three methods [22 – 24] try to space out room from the encrypted image directly, which follows the idea of compressing the encrypted image [25,26].”
• Reversible Data Hiding in Encrypted Images With Distributed Source Encoding (2016, background): “Some works have been done for data processing in an encrypted domain, for example, compressing encrypted images [2]–[4], adding a watermark into the encrypted image [5], [6], and reversibly hiding data into the encrypted image [7]–[13].”
• A privacy-preserving protocol for neural-network-based computation (2006, neutral): “MM&Sec 06, September 26 27, 2006, Geneva, Switzerland.”