MASSIVE OPEN ONLINE COURSES (MOOCS), AS A NEW EDUCATIONAL TREND, ATTRACT WORLDWIDE A HUGE NUMBER OF LEARNERS GIVING THEM APPROPRIATE KNOWLEDGE ACCORDING TO PERSONAL NEEDS. LEARNERS ARE STIMULATED TO BE ACTIVE PARTICIPANTS AND FACILITATED TO CREATE DISTRIBUTED LEARNING COMMUNITIES THROUGH: SHARING OPINIONS AND SETTING PREFERENCES; ANALYZING, ANNOTATING AND TRANSLATING LEARNING MATERIALS; PARTICIPATING IN INTERACTIVE GAMING EXERCISES; COMMUNICATING WITH OTHER PARTICIPANTS, TUTORS AND GUEST LECTURES; PREPARING AND SUBMITTING PRACTICAL ASSIGNMENTS; GROUP WORKING AND PEER REWIEVING; PROPOSING OTHER USEFUL (OPEN) EDUCATIONAL RESOURCES; PUBLISHING INSIGHTS ON DIFFERENT SOCIAL MEDIA PLATFORMS. THEREFORE, DURING PARTICIPATING IN MASSIVE OPEN ONLINE COURSES A WIDE VARIETY OF STUDENTS' PERSONAL AND ACTIVITY DATA ARE COLLECTED AND USED FOR DIFFERENT PURPOSES BY TUTORS, HOSTING PLATFORMS AND POSSIBLE THIRD PARTY ACTORS, SUCH AS UNIVERSITIES OR COMPANIES. PART OF THIS BIG DATA IS PROCESSED BY THE LEARNING ANALYTICS MODULES, IN ORDER TO UNDERSTAND AND OPTIMIZE THE LEARNING PROCESS. THE QUESTIONS THAT ARISE ARE: - WHAT KIND OF STUDENTS' PRIVATE DATA ARE SHARED AND WHY?, - WHAT ARE THE PRIVACY MODELS PROVIDED BY MOOCS?, - WHAT DATA IS NEEDED FOR LEARNING ANALYTICS MODULES?, - ARE THERE ANY THIRD PARTY ACTORS PROCESSING THE STUDENTS' DATA AND WHY?, - WHICH ARE THE RELATIONS BETWEEN LEARNING ANALYTICS AND DATA PRIVACY?, - IS IT POSSIBLE FOR THE LEARNERS TO BE PROTECTED?. THE PAPER IS LOOKING FOR ANSWERS OF THE ABOVE PLACED QUESTIONS SUMMARIZING AND ANALYZING THE CURRENT PROJECTS AND PRACTICES, BEST LEARNING CASES AND RESEARCH STANDPOINTS. THE FINDINGS ARE USED FOR DEVELOPMENT OF A MODEL PRESENTING A POSSIBILITY TO ENSURE DATA PRIVACY IN MOOCS. THE ANALYSIS, MODEL AND CONCLUSIONS INDICATE THE POSITIVE FEATURES AND BANDGAPS IN THE AREA OF DATA PRIVACY IN THE CONTEXT OF MOOCS. THEY COULD BE USED IN THE FORM OF DIRECTIONS FOR DEVELOPERS OF MOOCS WITH AIM TO GUARANTEE DATA PRIVACY OF LEARNERS.