The inference problem in multilevel secure databases

Conventional access control models, such as role-based access control, protect sensitive data from unauthorized disclosure via direct accesses, however, they fail to prevent unauthorized disclosure happening through indirect accesses. Indirect data disclosure via inference channels occurs when sensitive information can be inferred from nonsensitive data and metadata, which is also known as “the inference problem”. This problem has draw n much attention from researcher in the database community due to its great compromise of data security. It has been studied under four settings according to where it occurs. They are statistical databases, multilevel secure databases, data mining, and web-based applications. This thesis investigates previous efforts dedicated to inference problems in multilevel secure databases, and presents the latest findings of our research on this problem. Our contribution includes two methods. One is a dynamic control over this problem, which designs a set of accessing key distribution schemes to remove inference after all inference channels in the database has been identified. The other combines rough sets and entropies to form a computational solution to detect and remove inferences, which for the first time provides an integrated solution to the inference problem. Comparison with previous work has also been done, and we have proved both of them are effective and easy to implement. Since the inference problem is described as a problem of detecting and removing inference channels, this thesis contains two main parts: inference detecting techniques and inference removing techniques. In both two aspects, some techniques are selectively but extensively examined

Securing Databases from Probabilistic Inference

Databases can leak confidential information when users combine query results with probabilistic data dependencies and prior knowledge. Current research offers mechanisms that either handle a limited class of dependencies or lack tractable enforcement algorithms. We propose a foundation for Database Inference Control based on ProbLog, a probabilistic logic programming language. We leverage this foundation to develop Angerona, a provably secure enforcement mechanism that prevents information leakage in the presence of probabilistic dependencies. We then provide a tractable inference algorithm for a practically relevant fragment of ProbLog. We empirically evaluate Angerona's performance showing that it scales to relevant security-critical problems.Comment: A short version of this paper has been accepted at the 30th IEEE Computer Security Foundations Symposium (CSF 2017

Cover Story Management

International audienceIn a multilevel database, cover stories are usually managed using the ambiguous technique of polyinstantiation. In this paper, we define a new technique to manage cover stories and propose a formal representation of a multilevel database containing cover stories. Our model aims to be a generic model, that is, it can be interpreted for any kind of database (e.g. relational, object- oriented etc). We then consider the problem of updating a multilevel database containing cover stories managed with our technique

Logical Foundations of Multilevel Databases

International audienceIn this paper, we propose a formal model for multilevel databases. This model aims at being a generic model, that is it can be interpreted for any kind of database (relational, object-oriented...). Our model has three layers. The first layer corresponds to a model for a non-protected database. The second layer corresponds to a model for a multilevel database. In this second layer, we propose a list of theorems that must be respected in order to build a secure multilevel database. We also propose a new solution to manage cover stories without using the ambiguous technique of polyinstantiation. The third layer corresponds to a model for a MultiView database, that is, a database that provides at each security level a consistent view of the multilevel database. Finally, as an illustration, we interpret our 3-layer model in the case of an object-oriented database