Summary
Top-Down Specialization, a data anonymization algorithm that balances data utility and privacy, is implemented in parallel on Apache Spark and evaluated on datasets of up to 20 million rows across cluster environments. Speedups by data size, partition sizing, driver versus executor work and scale-up experiments are analyzed.
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