Training-data deduplication: deleting duplicate samples cuts memorization and extraction risk a lot — but it isn't a formal guarantee
In one sentence dedup is a high-return move that cuts memorization / extraction / membership-inference risk a lot; but it lowers frequency and probability, not a formal guarantee — a rare sample appearing once can still be memorized; for a formal guarantee, stack DP.