Research
Instructive of Ooze Information
We study the following problem: A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). Some of the data are leaked and bring into being in an unconstitutional place (e.g., on the web or somebody’s laptop). The distributor must evaluate the likelihood that the leaked data came from one or more agents, as opposed to having been independently gathered by other means. We propose data distribution strategies (across the agents) that improve the likelihood of identifying leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In some cases, we can also inject “realistic but replica†data records to further improve our chances of detecting leakage and identifying the guilty party. In the course of doing business, sometimes sensitive data must be handed over to supposedly trusted third parties. For example, a hospital may give patient records to Researchers who will devise new treatments. Similarly, a company may have partnerships with other companies that require sharing customer data. Another enterprise may outsource its data processing, so data must be given to various other companies. There always remains a risk of data getting leaked from the agent. Perturbation is a very valuable technique where the data are modified and made “less sensitive†before being handed to agents. For example, one can add random noise to certain attributes, or one can replace exact values by ranges. But this technique requires modification of data. Leakage detection is handled by watermarking, e.g., a unique code is implanted in each distributed copy. If that copy is later discovered in the hands of an unconstitutional party, the leaker can be identified. But again it requires code modification. Watermarks can sometimes be destroyed if the data recipient is malicious.
Artificial Intelligence an Essential Expected Computer World Surveillance
A position paper toward an important and urgent discussion on how best uses the potential of Artificial Intelligence in the context of Computer World surveillance. AI is often cited in papers on Computer World surveillance. But what is meant is using pre-existing AI techniques in Computer World surveillance. AI techniques are established around applications. Computer World surveillance has never been an area of deliberation in AI. In this paper we argue that Computer World surveillance calls for new and specific AI techniques developed with that kind of application in mind. In practice, this paper is based on a broad overview of different slants, which have the budding to be game changers in Computer World surveillance. This paper focuses on web solicitation security and supporters the use of Knowledge Based Systems, probabilistic reasoning and Bayesian apprising to control the probability of false positives and false denials.
