On the Security of the K Minimum Values (KMV) Sketch

Data sketches are widely used to accelerate operations in big data analytics. For example, algorithms use compute the cardinality of a set, or similarity between two sets. Sketches achieve significant reductions computing time and storage requirements by providing probabilistic estimates rather than exact values. In many applications, an estimate is sufficient thus, it possible trade accuracy for computational complexity; this enables sketches. However, structures may create security issues because attacker manipulate such way that produce incorrect estimate. could potentially inflate number distinct users increase its revenues popularity. Recent works have shown can Hyperloglog, sketch estimate, with no knowledge implementation details. This paper considers K Minimum Values (KMV), also implement both estimates. Next sections characterize vulnerabilities at implementation-independent level, attacks formulated as part novel adversary model manipulates Therefore, pursues analysis simulation; results suggest vulnerable attacks, reduction occur. The execution against KMV Apache DataSketches library validates these scenarios. Experiments show excellent agreement theory experimental results.