  This paper is concerned with enhancing data utility in the privacy watchdog
method for attaining information-theoretic privacy. For a specific privacy
constraint, the watchdog method filters out the high-risk data symbols through
applying a uniform data regulation scheme, e.g., merging all high-risk symbols
together. While this method entirely trades the symbols resolution off for
privacy, we show that the data utility can be greatly improved by partitioning
the high-risk symbols set and individually privatizing each subset. We further
propose an agglomerative merging algorithm that finds a suitable partition of
high-risk symbols: it starts with a singleton high-risk symbol, which is
iteratively fused with others until the resulting subsets are
private.~Numerical simulations demonstrate the efficacy of this algorithm in
privately achieving higher utilities in the watchdog scheme.
