{'fraction_non_alphanumeric': 0.01800947867298578, 'fraction_numerical': 0.0, 'mean_word_length': 5.439024390243903, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Achieving secure and accurate friend discovery based on friend-of-friend's recommendations

Friend discovery has been one of the hot topics in our social activities over the past decade. Mobile users have more opportunities to discover and make new social interactions with others in vicinity to build and extend their social communities. However, the inevitable information releasing conflicts with the increasing privacy concerns. In this paper, we employ the concept of friend-of-friend and design a secure and accurate friend discovery for privacy-aware mobile users in Proximity-based Mobile Social Networks (PMSNs). We first construct a novel similarity function with fully considering the number of common attributes, the corresponding priorities and the ratio of matched attributes over all the inputs. Then we develop a secure friend recommendation phase based on a carefully combination of the commutative encryption function and the bilinear pairings. The security and performance are thoroughly analyzed and evaluated via detailed simulations.