Recently, a significant amount of interest has been developed in motivating physicians to use e-health technology (especially Electronic Medical Records (EMR) systems). An important utility of such EMR systems is: a next generation of Clinical Decision Support Systems (CDSS) will extract knowledge from these electronic medical records to enable physicians to do accurate and effective diagnosis. It is anticipated that in future such medical records will be shared through cloud among different physicians to improve the quality of health care. Therefore, right protection of medical records is important to protect their ownership once they are shared with third parties. Watermarking is a proven well known technique to achieve this objective. The challenges associated with watermarking of EMR systems are: (1) some fields in EMR are more relevant in the diagnosis process; as a result, small variations in them could change the diagnosis, and (2) a misdiagnosis might not only result in a life threatening scenario but also might lead to significant costs of the treatment for the patients. The major contribution of this paper is an information-preserving watermarking scheme to address the above-mentioned challenges. We model the watermarking process as a constrained optimization problem. We demonstrate, through experiments, that our scheme not only preserves the diagnosis accuracy but is also resilient to well known attacks for corrupting the watermark. Last but not least, we also compare our scheme with a well known threshold-based scheme to evaluate relative merits of a classifier. Our pilot studies reveal that -- using proposed information-preserving scheme -- the overall classification accuracy is never degraded by more than 1 . In comparison, the diagnosis accuracy, using the threshold-based technique, is degraded by more than 18 in a worst case scenario.
The proposed technique uses an optimization technique to first create a watermark which ensures that data usability constraints are not violated. Once the watermark is created, we embed it in realtime into an EMR system. In comparison, other techniques @cite_19 use optimization techniques during watermark embedding and hence they take a significant amount of time. As a consequence, they are not suitable for realtime insertion of watermark into an EMR system.
@cite_19
2136078037 2560266880 2129002694 2768064608
Proving ownership rights on outsourced relational databases is a crucial issue in today's internet-based application environments and in many content distribution applications. In this paper, we present a mechanism for proof of ownership based on the secure embedding of a robust imperceptible watermark in relational data. We formulate the watermarking of relational databases as a constrained optimization problem and discuss efficient techniques to solve the optimization problem and to handle the constraints. Our watermarking technique is resilient to watermark synchronization errors because it uses a partitioning approach that does not require marker tuples. Our approach overcomes a major weakness in previously proposed watermarking techniques. Watermark decoding is based on a threshold-based technique characterized by an optimal threshold that minimizes the probability of decoding errors. We implemented a proof of concept implementation of our watermarking technique and showed by experimental results that our technique is resilient to tuple deletion, alteration, and insertion attacks. Existing techniques of watermarking make use of transform domain to have better robustness towards attacks. Here, we propose a novel learning based auto-encoder Convolutional Neural Network (CNN) for non-blind watermarking which outperforms the existing frequency domain techniques in terms of imperceptibility and robustness adding new dimension of usage of CNNs towards security. As these CNNs efficiently learn the features and represent the input at the output, they find applications in all the fields of science. Code book images of different size are generated using the proposed architecture and subjected to different attacks. Results of the proposed method are compared with state of the art methods at different noises and attacks such as Gaussian, speckle, compression effects, cropping, filtering, etc. The proposed scheme is validated against various possible attacks and its out-performance with state of the art methods is presented. Further, transfer learning capabilities of auto-encoder CNN for efficient way of learning new code book is presented. The inability of intruder towards retrieval of data without the knowledge of architecture and keys employed is also discussed. Traditional watermarking schemes are sensitive to geometric distortions, in which synchronisation for recovering embedded information is a challenging task because of the disorder caused by rotation, scaling or translation (RST). The existing RST-resistant watermarking methods still have limitations with respect to robustness, capacity or fidelity. In this study, the authors address several major problems in RST-invariant watermarking. The first point is how to take advantage of the high RST resilience of scale-invariant feature transform (SIFT) features, which show good performance in terms of RST-resistant pattern recognition. Since many keypoint-based watermarking methods do not discuss cropping attacks, the second issue discussed in this study is how to resist cropping using a human visual system (HVS), which also helps us to eliminate computational complexity. The third issue is the investigation of an HVS-based watermarking strategy for extracting only feature points in the human attentive area. Lastly, a variable-length watermark synchronisation algorithm using dynamic programming is proposed. Experimental results show that the proposed algorithms are practical and show superior performance in comparison with many existing works in terms of watermark capacity, watermark transparency, and the resistance to RST attacks. The state of the art performance of deep learning models comes at a high cost for companies and institutions, due to the tedious data collection and the heavy processing requirements. Recently, (2017) proposed to watermark convolutional neural networks by embedding information into their weights. While this is a clear progress towards model protection, this technique solely allows for extracting the watermark from a network that one accesses locally and entirely. This is a clear impediment, as leaked models can be re-used privately, and thus not released publicly for ownership inspection. Instead, we aim at allowing the extraction of the watermark from a neural network (or any other machine learning model) that is operated remotely, and available through a service API. To this end, we propose to operate on the model's action itself, tweaking slightly its decision frontiers so that a set of specific queries convey the desired information. In present paper, we formally introduce the problem and propose a novel zero-bit watermarking algorithm that makes use of adversarial model examples. While limiting the loss of performance of the protected model, this algorithm allows subsequent extraction of the watermark using only few remote queries. We experiment this approach on the MNIST dataset with three types of neural networks, demonstrating that e.g., watermarking with 100 images incurs a slight accuracy degradation, while being resilient to most removal attacks.