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Robustness of the Hash Function Percentage of the Pixel Changed in Data Embedding 17.3.3.3 Time Required to Run the Complete Image/Data Security Assurance 17.3.4 Limitations of the Digital Envelope Method DICOM Security Pro les 17.4.1 Current DICOM Security Pro les 17.4.1.1 Secure Use Pro les 17.4.1.2 Secure Transport Connection Pro les 17.4.1.3 Digital Signature Pro les 17.4.1.4 Media Security Pro les 17.4.2 Some New DICOM Security on the Horizon HIPAA and Its Impacts on PACS Security PACS Security Server and Authority for Assuring Image Authenticity and Integrity 17.6.1 Comparison of Image-Embedded DE Method and DICOM Security 17.6.2 An Image Security System in a PACS Environment Lossless Digital Signature Embedding Methods for Assuring 2-D and 3-D Medical Image Integrity 17.7.1 Lossless Digital Signature Embedding 17.7.2 Procedures and Methods 17.7.3 General LDSE Method Two-dimensional and Three-dimensional Lossless Digital Signature Embedding Method 17.8.1 Two-dimensional LDSERS Algorithm 17.8.1.1 General 2-D Method 17.8.1.2 Signing and Embedding 17.8.1.3 Extracting and Verifying 17.8.2 Three-dimensional LDSERS Algorithm 17.8.2.1 General 3-D LDSE Method 17.8.2.2 Signing and Embedding 17.8.2.3 Extracting and Verifying From a Three-dimensional Volume to Two-dimensional Image(S) 17.9.1 Extract Sectional Images from 3-D Volume with LDSERS 17.9.2 Two-dimensional LDSERS Results 17.9.2.1 Embedded LDSERS Pattern 17.9.2.2 Time Performance of 2-D LDSERS 17.9.3 Three-dimensional LDSERS Results 17.9.3.1 Embedded LDSERS Pattern 17.9.3.2 Time Performance of 3-D LDSERS Application of Two-dimensional and Three-dimensional LDSE in Clinical Image Data Flow



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percentage impact is only 25 percent. One way of collecting data on the percentage of customers who called back is to look at the amount of revenue generated above and beyond the normal orders immediately following the outage. If it is 50 percent more, that means 50 percent of the people called back. Several other effects of downtime are impossible to quantify. Some customers who were merely inconvenienced may tell the story to friends and recommend to them never to shop at that place. Percentage impact is difficult to estimate and is different during different outages. The results from the equations convey only part of a company s losses. Satisfied customers become loyal customers and dissatisfied customers do not. Let s say a customer would have made a $100 purchase and would have repeated that just once a year. Using a discounted cash flow rate of just 15 percent, the present value of those purchases over a 20-year period would have been $719. The company, therefore, suffered a total loss of more than seven times the first lost sale value. Downtime causes several other losses that are difficult to predict. Companies that host applications or servers for customers have strict service level agreements (SLAs) that require them to credit customer accounts upon incurring any service disruption. Downtime also causes missed deadlines and penalties. It adversely affects stock price, goodwill among customers and partners, and employee morale. For servers connected to the Internet, the greatest cause for concern is security. Hackers are quick to take advantage of vulnerabilities. Malicious code attacks have significant economic impact. Table 1-4 shows the adverse economic impact caused by cyber attacks. Table 1-5 shows the economic impact of specific incidents. To date, the I Love You Bug outbreak in 2000 has caused the highest financial damage.





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Adachi I, Quantitative Measurement of Aberration by Ronchi Test, Atti Fond Giorgio Contrib 1st Naz Ottica, 15, 550 585 (1960b) Adachi I, The Recent History of the Grating Interferometer and Its Applications, Atti Fond Giorgio Ronchi Contrib 1st Naz Ottica, 17, 252 259 (1962a) Adachi I, Measurement of Transfer Function of the Ronchi Test, Atti Fond Giorgio Ronchi Contrib 1st Naz Ottica, 17, 523 534 (1962b) Adachi I, The Diffraction Theory of the Ronchi Test, Atti Fond Giorgio Ronchi Contrib 1st Naz Ottica, 18, 344 349 (1963) Agureev V V, Refractive-Index Distribution in Gradient-Index Fibers, Studied by Means of Shearing Interferometry, J Opt Tech, 62, 375 378 (1995) Agureev V V, V N Morozov, G O Karapetyan and S N Ivashevskii, Refractive-Index Distribution in Gradient-Index Fibers, Studied by Means of Shearing Interferometry, J Opt Tech, 62, 375 378 (1995) Anderson J A.

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