No Arabic abstract
Recently, a medical privacy protection scheme (MPPS) based on DNA coding and chaos was proposed in [IEEETrans. Nanobioscience, vol. 16, pp. 850--858, 2017], which uses two coupled chaotic system to generate cryptographic primitives to encrypt color DICOM image. Relying on several statistical experimental results and some theoretical analyses, the designers of MPPS claimed that it is secure against chosen-plaintext attack and the other classic attacks. However, the above conclusion is insufficient without cryptanalysis. In this paper, we first study some properties of MPPS and DNA coding and then propose a chosen-plaintext attack to reveal its equivalent secret-key. It is proved that the attack only needs $lceil log_{256}(3cdot Mcdot N)rceil+4$ chosen plain-images, where $M times N$ is the size of the RGB color image, and ``3 is the number of color channels. Also, the other claimed superiorities are questioned from the viewpoint of modern cryptography. Both theoretical and experimental results are provided to support the feasibility of the attack and the other reported security defects. The proposed cryptanalysis work will promote the proper application of DNA encoding in protecting multimedia data including the DICOM image.
Privacy amplification is an indispensable step in the post-processing of quantum key distribution, which can be used to compress the redundancy of shared key and improve the security level of the key. The commonly used privacy amplification is based on the random selection of universal hash functions, which needs the help of an additional random source, while it does not exist in general. In this paper, we propose a privacy amplification scheme based on composite coding, which is an extension of quantum CSS codes to classical linear codes. Compared with the universal hashing function, the proposed scheme does not need other random sources, and the randomness can be completely provided by the qubit string. Furthermore, the information-theoretic bound for the extraction of the key is obvious in composite coding.
This paper analyzes the security of an image encryption algorithm proposed by Ye and Huang [textit{IEEE MultiMedia}, vol. 23, pp. 64-71, 2016]. The Ye-Huang algorithm uses electrocardiography (ECG) signals to generate the initial key for a chaotic system and applies an autoblocking method to divide a plain image into blocks of certain sizes suitable for subsequent encryption. The designers claimed that the proposed algorithm is strong and flexible enough for practical applications. In this paper, we perform a thorough analysis of their algorithm from the view point of modern cryptography. We find it is vulnerable to the known plaintext attack: based on one pair of a known plain-image and its corresponding cipher-image, an adversary is able to derive a mask image, which can be used as an equivalent secret key to successfully decrypt other cipher-images encrypted under the same key with a non-negligible probability of 1/256. Using this as a typical counterexample, we summarize security defects in the design of the Ye-Huang algorithm. The lessons are generally applicable to many other image encryption schemes.
The Internet is a ubiquitous and affordable communications network suited for e-commerce and medical image communications. Security has become a major issue as data communication channels can be intruded by intruders during transmission. Though, different methods have been proposed and used to protect the transmission of data from illegal and unauthorized access, code breakers have come up with various methods to crack them. DNA based Cryptography brings forward a new hope for unbreakable algorithms. This paper outlines an encryption scheme with DNA technology and JPEG Zigzag Coding for Secure Transmission of Images.
We firstly suggest privacy protection cache policy applying the duty to delete personal information on a hybrid main memory system. This cache policy includes generating random data and overwriting the random data into the personal information. Proposed cache policy is more economical and effective regarding perfect deletion of data.
Privacy protection is an important research area, which is especially critical in this big data era. To a large extent, the privacy of visual classification data is mainly in the mapping between the image and its corresponding label, since this relation provides a great amount of information and can be used in other scenarios. In this paper, we propose the mapping distortion based protection (MDP) and its augmentation-based extension (AugMDP) to protect the data privacy by modifying the original dataset. In the modified dataset generated by MDP, the image and its label are not consistent ($e.g.$, a cat-like image is labeled as the dog), whereas the DNNs trained on it can still achieve good performance on benign testing set. As such, this method can protect privacy when the dataset is leaked. Extensive experiments are conducted, which verify the effectiveness and feasibility of our method. The code for reproducing main results is available at url{https://github.com/PerdonLiu/Visual-Privacy-Protection-via-Mapping-Distortion}.