With the rapid expansion of multimedia data,protecting digital information has become increasingly critical.Reversible data hiding offers an effective solution by allowing sensitive information to be embedded in multi...With the rapid expansion of multimedia data,protecting digital information has become increasingly critical.Reversible data hiding offers an effective solution by allowing sensitive information to be embedded in multimedia files while enabling full recovery of the original data after extraction.Audio,as a vital medium in communication,entertainment,and information sharing,demands the same level of security as images.However,embedding data in encrypted audio poses unique challenges due to the trade-offs between security,data integrity,and embedding capacity.This paper presents a novel interpolation-based reversible data hiding algorithm for encrypted audio that achieves scalable embedding capacity.By increasing sample density through interpolation,embedding opportunities are significantly enhanced while maintaining encryption throughout the process.The method further integrates multiple most significant bit(multi-MSB)prediction and Huffman coding to optimize compression and embedding efficiency.Experimental results on standard audio datasets demonstrate the proposed algorithm’s ability to embed up to 12.47 bits per sample with over 9.26 bits per sample available for pure embedding capacity,while preserving full reversibility.These results confirm the method’s suitability for secure applications that demand high embedding capacity and perfect reconstruction of original audio.This work advances reversible data hiding in encrypted audio by offering a secure,efficient,and fully reversible data hiding framework.展开更多
Anonymized data publication has received considerable attention from the research community in recent years. For numerical sensitive attributes, most of the existing privacy-preserving data publishing techniques conce...Anonymized data publication has received considerable attention from the research community in recent years. For numerical sensitive attributes, most of the existing privacy-preserving data publishing techniques concentrate on microdata with multiple categorical sensitive attributes or only one numerical sensitive attribute. However, many real-world applications can contain multiple numerical sensitive attributes. Directly applying the existing privacy-preserving techniques for single-numerical-sensitive-attribute and multiple-categorical-sensitive- attributes often causes unexpected disclosure of private information. These techniques are particularly prone to the proximity breach, which is a privacy threat specific to numerical sensitive attributes in data publication, in this paper, we propose a privacy-preserving data publishing method, namely MNSACM, which uses the ideas of clustering and Multi-Sensitive Bucketization （MSB） to publish microdata with multiple numerical sensitive attributes. We use an example to show the effectiveness of this method in privacy protection when using multiple numerical sensitive attributes.展开更多
基金funded by theNational Science and Technology Council of Taiwan under the grant number NSTC 113-2221-E-035-058.
文摘With the rapid expansion of multimedia data,protecting digital information has become increasingly critical.Reversible data hiding offers an effective solution by allowing sensitive information to be embedded in multimedia files while enabling full recovery of the original data after extraction.Audio,as a vital medium in communication,entertainment,and information sharing,demands the same level of security as images.However,embedding data in encrypted audio poses unique challenges due to the trade-offs between security,data integrity,and embedding capacity.This paper presents a novel interpolation-based reversible data hiding algorithm for encrypted audio that achieves scalable embedding capacity.By increasing sample density through interpolation,embedding opportunities are significantly enhanced while maintaining encryption throughout the process.The method further integrates multiple most significant bit(multi-MSB)prediction and Huffman coding to optimize compression and embedding efficiency.Experimental results on standard audio datasets demonstrate the proposed algorithm’s ability to embed up to 12.47 bits per sample with over 9.26 bits per sample available for pure embedding capacity,while preserving full reversibility.These results confirm the method’s suitability for secure applications that demand high embedding capacity and perfect reconstruction of original audio.This work advances reversible data hiding in encrypted audio by offering a secure,efficient,and fully reversible data hiding framework.
基金supported by the National Natural Science Foundation of China (No. 61170232)the 985 Project Funding of Sun Yat-sen University+1 种基金State Key Laboratory of Rail Traffic Control and Safety Independent Research (No. RS2012K011)Ministry of Education Funds for Innovative Groups (No. 241147529)
文摘Anonymized data publication has received considerable attention from the research community in recent years. For numerical sensitive attributes, most of the existing privacy-preserving data publishing techniques concentrate on microdata with multiple categorical sensitive attributes or only one numerical sensitive attribute. However, many real-world applications can contain multiple numerical sensitive attributes. Directly applying the existing privacy-preserving techniques for single-numerical-sensitive-attribute and multiple-categorical-sensitive- attributes often causes unexpected disclosure of private information. These techniques are particularly prone to the proximity breach, which is a privacy threat specific to numerical sensitive attributes in data publication, in this paper, we propose a privacy-preserving data publishing method, namely MNSACM, which uses the ideas of clustering and Multi-Sensitive Bucketization （MSB） to publish microdata with multiple numerical sensitive attributes. We use an example to show the effectiveness of this method in privacy protection when using multiple numerical sensitive attributes.
