SLIDE: An Efficient Secure Linguistic Steganography Detection Protocol

Linguistic steganography detection aims at distinguishing between normal text and stego-text. In this paper, based on homomorphic cryptosystem, we propose an efficient secure protocol for linguistic steganography detection. The protocol involves a vendor holding a private detector of linguistic steganography and a user in possession of some private text documents consisting of stego-text and normal text. By cooperatively performing the secure two-party protocol, the user can securely obtain the detection results of his private documents returned by the vendor’s remote detector while both vendor and user learn nothing about the privacy of each other. It is shown the proposed protocol is still secure against probe attack. Experiment result and theoretical analysis confirm the efficiency, correctness, security, computation complexity and communication overheads of our scheme.

[1]  Jian Wang,et al.  Efficient Encrypted Data Comparison Through a Hybrid Method , 2017, J. Inf. Sci. Eng..

[2]  Jie Wu,et al.  Friendship-based location privacy in Mobile Social Networks , 2011, Int. J. Secur. Networks.

[3]  Chris Clifton,et al.  Efficient privacy-preserving similar document detection , 2010, The VLDB Journal.

[4]  Yang Yi-xian.  Research on the detecting algorithm of text document information hiding , 2004 .

[5]  Mauro Barni,et al.  A privacy-preserving protocol for neural-network-based computation , 2006, MM&Sec '06.

[6]  Liusheng Huang,et al.  Relation of PPAtMP and scalar product protocol and their applications , 2010, The IEEE symposium on Computers and Communications.

[7]  Tsuyoshi Takagi,et al.  Secure and controllable k-NN query over encrypted cloud data with key confidentiality , 2016, J. Parallel Distributed Comput..

[8]  Chris Clifton,et al.  Similar Document Detection with Limited Information Disclosure , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[9]  Aniello Castiglione,et al.  Efficient k-NN query over encrypted data in cloud with limited key-disclosure and offline data owner , 2017, Comput. Secur..

[10]  Jian Wang,et al.  Practical Secure Naïve Bayesian Classification Over Encrypted Big Data in Cloud , 2017, Int. J. Found. Comput. Sci..

[11]  Yang Wei,et al.  Text Information Hiding Detecting Algorithm Based on Statistics , 2008 .

[12]  Edward J. Delp,et al.  Attacks on lexical natural language steganography systems , 2006, Electronic Imaging.

[13]  Jie Wu,et al.  Privacy-preserved data publishing of evolving online social networks , 2016 .

[14]  Vitaly Shmatikov,et al.  Privacy-preserving remote diagnostics , 2007, CCS '07.

[15]  Liusheng Huang,et al.  Linguistic Steganography Detection Using Statistical Characteristics of Correlations between Words , 2008, Information Hiding.

[16]  Lu Zhou,et al.  Efficiently and securely harnessing cloud to solve linear regression and other matrix operations , 2018, Future Gener. Comput. Syst..

[17]  Oded Goldreich,et al.  Foundations of Cryptography: Volume 2, Basic Applications , 2004 .

[18]  Pascal Paillier,et al.  Public-Key Cryptosystems Based on Composite Degree Residuosity Classes , 1999, EUROCRYPT.

[19]  Wenzhong Li,et al.  Navigation-driven handoff minimization in wireless networks , 2016, J. Netw. Comput. Appl..