Inference in MLS database systems

Marks, Donald G.

doi:10.1109/69.485628

Cited by 50 publications

(34 citation statements)

References 4 publications

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“…The non-probabilistic metrics are mainly used in the fields of inference problem of statistical databases [1,17,16,26], multilevel databases [20,5] and general purpose databases [7,5,27]. The most often used one is that if the private value of an individual cannot be uniquely inferred, released data about the individual are considered safe [1,20,7,17,5,16].…”

Section: Related Workmentioning

confidence: 99%

“…The most often used one is that if the private value of an individual cannot be uniquely inferred, released data about the individual are considered safe [1,20,7,17,5,16]. The other one is the cardinality of the set of possible private values for each individual, among which attackers cannot determine which one is the actual one [26,27] (The metric used in [27] is an uncertainty metric in spite of the notion of k-anonymity introduced).…”

Section: Related Workmentioning

confidence: 99%

“…These metrics can be classified into two categories: non-probabilistic and probabilistic. The non-probabilistic metrics are based on whether the private value of an individual can be uniquely inferred from the released data [1,20,7,17,5,16] or whether the cardinality of the set of possible private values inferred for an individual is large enough [26,27]. The probabilistic metrics are based on some characteristics of the probability distribution of…”

Section: Introductionmentioning

confidence: 99%

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Indistinguishability: The Other Aspect of Privacy

Yao

Wang

et al. 2006

Lecture Notes in Computer Science

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Abstract. Uncertainty and indistinguishability are two independent aspects of privacy. Uncertainty refers to the property that the attacker cannot tell which private value, among a group of values, an individual actually has, and indistinguishability refers to the property that the attacker cannot see the difference among a group of individuals. While uncertainty has been well studied and applied to many scenarios, to date, the only effort in providing indistinguishability has been the well-known notion of k-anonymity. However, k-anonymity only applies to anonymized tables. This paper defines indistinguishability for general situations based on the symmetry among the possible private values associated with individuals. The paper then discusses computational complexities of and provides practical algorithms for checking whether a set of database views provides enough indistinguishability.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Indistinguishability: The Other Aspect of Privacy

Yao

Wang

et al. 2006

Lecture Notes in Computer Science

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“…Multiple tables can be modeled as a universal relation as discussed in [9]. The goal of our inference detection system is to detect if a user can infer data using a series of queries.…”

Section: Notationsmentioning

confidence: 99%

“…Hale et al incorporate imprecise and fuzzy database relations into their inference channel detection system [4]. Marks develops an inference detection system that prevents alI possible inference by monitoring user queries with select clauses of the form "Ai = ai", where ai is a constant [9]. Chang et al use Bayesian estimation and network techniques to estimate missing data in the database [2].…”

Section: Introductionmentioning

confidence: 99%

The Design and Implementation of a Data Level Database Inference Detection System

Yip¹,

Levitt²

1999

Database Security XII

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Inference is a way to subvert access control mechanisms of database systems. Most existing work on inference detection relies on analyzing functional dependencies in the database schema. This paper is an extension to our earlier effort in developing a data level inference detection system [13]. In this paper, we introduce the split query inference rule, make an extension to the overlapping inference rule, and provide an in depth discussion on the applications of the inference rules on union queries. Data level inference detection is inevitably expensive. We have developed a prototype of the inference detection system to evaluate its performance. The result shows that the system performs better with larger number of attributes and records in the database, and smaller number of projected attributes and return tuples of the queries. Therefore, the inference detection system could be practical when users retrieve a small amount of data compare to the size of the database. INTRODUCTIONAn inference occurs when a user infers data that the user is not allowed to access. In multilevel secure database systems, early work on inference detection employs a graph to represent the functional dependencies among attributes in the database schema. An inference occurs if there exists two paths between two attributes (or composite attributes), and the two paths are labeled at different S. Jajodia (ed.), Database Security XII

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Human–Machine Interfaces

2021

Electronics in Advanced Research Industries

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Inference in MLS database systems

Cited by 50 publications

References 4 publications

Indistinguishability: The Other Aspect of Privacy

Indistinguishability: The Other Aspect of Privacy

The Design and Implementation of a Data Level Database Inference Detection System

Human–Machine Interfaces

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