Middleware Support for Tunable Encryption

More and more effort is being spent on security improvements in today's computer environments, with the aim to achieve an appropriate level of security. However, for small computing devices it might be necessary to reduce the computational cost imposed by security in order to gain reasonable performance and/or energy consumption. To accomplish this selective encryption can be used, which provides confidentiality by only encrypting chosen parts of the information. Previous work on selective encryption has chiefly focused on how to reduce the computational cost while still making the information perceptually secure, but not on how computationally secure the selectively encrypted information is. Despite the efforts made and due to the harsh nature of computer security, good quantitative assessment methods for computer security are still lacking. Inventing new ways of measuring security are therefore needed in order to better understand, assess, and improve the security of computer environments. Two proposed probabilistic quantitative security measures are entropy and guesswork. Entropy gives the average number of guesses in an optimal binary search attack, and guesswork gives the average number of guesses in an optimal linear search attack. In information theory, a considerable amount of research has been carried out on entropy and on entropy-based metrics. However, the same does not hold for guesswork.In this thesis, we evaluate the performance improvement when using the proposed generic selective encryption scheme. We also examine the confidentiality strength of selectively encrypted information by using and adopting entropy and guesswork. Moreover, since guesswork has been less theoretical investigated compared to entropy, we extend guesswork in several ways and investigate some of its behaviors.