In this paper, we propose a method of controlling personal data disclosure based on LooM (Loosely Managed Privacy Protection Method) that prevents a malicious third party from identifying a person when he/she gets context-aware services using personal data. The basic function of LooM quantitatively evaluates the anonymity level of a person who discloses his/her data, and controls the personal-data disclosure according to the level. LooM uses a normalized entropy value for quantifying the anonymity. In this version of the LooM, the disclosure control is accomplished by adding two new functions. One is an abstracting-function that generates abstractions (or summaries) from the raw personal data to reduce the danger that the malicious third party might identify the person who discloses his/her personal data to the party. The other function is a unique-value-masking function that hides the unique personal data in the database. These functions enhance the disclosure control mechanism of LooM. We evaluate the functions using simulation data and questionnaire data. Then, we confirm the effectiveness of the functions. Finally, we show a prototype of a crime-information-sharing service to confirm the feasibility of these functions.