This paper establishes the equivalence between Local Differential Privacy (LDP) and a global limit on learning any knowledge specific to a queried object. However, an output from an LDP query is not necessarily required to provide exact amount of knowledge equal to the upper bound of the learning limit. The LDP guarantee can overestimate the amount of knowledge gained by an analyst from some outputs. To address this issue, the least upper bound on the actual knowledge gain is derived and referred to as realized privacy loss. This measure is also shown to serve as an upper bound for the actual g-leakage in quantitative information flow. The gap between the LDP guarantee and realized privacy loss motivates the exploration of a more efficient privacy accounting for fully adaptive composition, where an adversary adaptively selects queries based on prior results. The Bayesian Privacy Filter is introduced to continuously accept queries until the realized privacy loss of the composed queries equals the LDP guarantee of the composition, enabling the full utilization of the privacy budget of an object. The realized privacy loss also functions as a privacy odometer for the composed queries, allowing the remaining privacy budget to accurately represent the capacity to accept new queries. Additionally, a branch-and-bound method is devised to compute the realized privacy loss when querying against continuous values. Experimental results indicate that Bayesian Privacy Filter outperforms the basic composition by a factor of one to four when composing linear and logistic regressions.