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A first step is in removing all records below a certain k-anonymity threshold from released data sets. Of course this brings in sample bias and reduces the usability of the set. Better educating researchers in the theory and practices of privacy is another thing that is urgently needed.

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The k-anonymity threshold changes as other datasets are released. If you are looking at a dataset with my zip code in it you can calculate a k-anonymous version of that dataset. As soon as somebody else releases another dataset with my zip code in it you must now consider both, and anything you released earlier is likely to be compromised.

Why would k-anonymity stay fixed? Maybe you could deanonymize the data later when better sets become available to correlate against.

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