Users routinely disclose personal information to obtain the benefits of Personalized Online Services. As a result, personal data is distributed across uncounted and unaccountable remote databases. Data mismanagement, as well as privacy and security flaws undermine individuals' control and privacy of their personal data. Yet revealing detailed private data does not necessarily yield useful service personalization; often this functionality is only modestly dependent upon the accuracy of user-supplied input. We demonstrate knowledge-based input generalization wherein systematically perturbed user data is supplied to a personalized service to gain forward privacy for the user, while retaining the utility of the service's results.

The Smalltalk-80* programming language includes dynamic storage allocation, full upward funargs, and universally polymorphic procedures; the Smalltalk-80 programming system features interactive execution with incremental compilation, and implementation portability. These features of modern programming systems are among the most difficult to implement efficiently, even individually. A new implementation of the Smalltalk-80 system, hosted on a small microprocessor-based computer, achieves high performance while retaining complete (object code) compatibility with existing implementations. This paper discusses the most significant optimization techniques developed over the course of the project, many of which are applicable to other languages. The key idea is to represent certain runtime state (both code and data) in more than one form, and to convert between forms when needed.