Aggregate Query Answering on Possibilistic Data with Cardinality Constraints

Abstract

Uncertainties in data arise for a number of reasons: when the data set is incomplete, contains conflicting information or has been deliberately perturbed or coarsened to remove sensitive details. An important case which arises in many real applications is when the data describes a set of possibilities, but with cardinality constraints. These constraints represent correlations between tuples encoding, e.g. that at most two possible records are correct, or that there is an (unknown) one-to-one mapping between a set of tuples and attribute values. Although there has been much effort to handle uncertain data, current systems are not equipped to handle such correlations, beyond simple mutual exclusion and co-existence constraints. Vitally, they have little support for efficiently handling aggregate queries on such data.In this paper, we aim to address some of these deficiencies, by introducing LICM (Linear Integer Constraint Model), which can succinctly represent many types of tuple correlations, particularly a class of cardinality constraints. We motivate and explain the model with examples from data cleaning and masking sensitive data, to show that it enables modeling and querying such data, which was not previously possible. We develop an efficient strategy to answer conjunctive and aggregate queries on possibilistic data by describing how to implement relational operators over data in the model. LICM compactly integrates the encoding of correlations, query answering and lineage recording. In combination with off-the-shelf linear integer programming solvers, our approach provides exact bounds for aggregate queries. Our prototype implementation demonstrates that query answering with LICM can be effective and scalable.

(τ, m)‐slicedBucket privacy model for sequential anonymization for improving privacy and utility

“…For implementing privacy and security in IoT and Big Data, numerous techniques exist in literature 17‐26 . For PPDP, a bunch of anonymization algorithms have also been proposed so far.…”

“…For implementing privacy and security in IoT and Big Data, numerous techniques exist in literature. [17][18][19][20][21][22][23][24][25][26] For PPDP, a bunch of anonymization algorithms have also been proposed so far. These PPDP approaches can be broadly categorized to semantic and syntactic privacy models.…”

“…In all these privacy approaches, the microdata are generalized into k-anonymized groups where every record is un-differentiable from at least k − 1 other records. The syntactic privacy algorithms can be further categorization into microaggregation, [22][23][24][25][26] anatomization, 27,28 and generalization. [29][30][31][32][33][34][35] Microaggregation replaces the QI values in an EC to the centroid of the EC.…”

On query execution over encrypted data

“…Hakan et al [11] have investigated on aggregation query over encrypted data in a cloud. Cormode et al [12] have introduced a Linear Integer Constraint Model to answer the conjunctive and aggregate queries over data. Optimizing the computation of range aggregate queries and aggregate continuous queries is discussed in [13] and [14], respectively.…”

Min-Based Symmetry Possibilistic Network Model for Representation of Uncertain Datamodels

“…We can convert the Uncertain Object model into Possibilistic database model [10,25,28,30] and vice versa. When, it is concerned that both are equivalent.…”