# BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services
## 2 Related Work  
### Privacy of Bloom Filter  
The role of social networking services tends to depend on the flexibility and availability of sharing locations with respect to privacy protection. [PERSON] et al. [23] developed opaque identities that were designed to help with the capability of sharing a person's presence amid trustworthy friends and untrustworthy strangers. Though the prior work resolved the problem of the lack of flexibility in the sharing of the locations regarding privacy protection, the stated method's strictness stops when it is directly used [24]. Likewise, a mechanism referred to as Mobshare [25], proposed by [PERSON] et al., has a privacy-preserving ability that is extremely flexible in sharing locations with both untrusted strangers and trusted social relations in mobile online social networks (mOSNs). Nevertheless, a Mobishare method is actually an extension of Smoke Screen though it uses an imitation tactic that included a fake identity, prevented revealing the user complete locations and identity amid social network providers and LBS suppliers. A later enhancement by [PERSON] et al. produced the ability to spread encryption designed to stop users' location discretion by letting users remove and add friends [26]. Although these improvements were implemented to address the numerous assaults on the LBS provider, an attacker can still find a way to access the information about the friends' relation to the user and their true time location. The Paillier-Cryptosystem, established by [PERSON] et al., might have stopped the problem. [PERSON] et al. introduced the idea of using Mobishare+ mechanism to employ a remote set of connection protocols to stop the social network server from learning individual data. Though this mechanism improved security, Mobishare+ needs to execute many complex coding and decoding operations that increase the overhead computations [27]. Other approaches are encryption-based, mainly in the cloud, that are used for the defense of privacy [28; 29; 30]. [PERSON] et al. [31] disclosed how the availability of cheap positioning systems has made it possible for them to be embedded in devices and other smartphones. This has led to the development of location-awareness applications, allowing users to demand personalized services that are dependent on their geographical location. However, only a small amount of information concerning the user's whereabouts should be available at any given time, since a user's geographical position is highly sensitive. Several applications such as the navigation system are dependent on the user's movement and require constant pursuing. Other applications only require knowing a user's position in relation to an individual area of interest. In [PERSON] et al. [32], BF are simple and space-efficient randomized data structures used to represent a certain group, such that any connection requests are maintained. Recently, the acceptance ability in their database and networking applications has been improved. A BF is composed of two major phases: programming and the membership request. From there, a new method for reducing hash table (HT) access time is introduced, which further integrates the HT into the filter. Hence, after the BF used for the external entry of a program, the following item is stored in an HT simultaneously. This method has the advantage of minimizing the average bucket size, maximum search length, and impact quantity. [PERSON] et al. [33] addressed issues related to the security of user location data through the use of BF, which can be described as a compressed data structure that is usually used to represent numerous groups. So, the spatial bloom filter (SBF) tends to be an expansion of the standard BF. The SBF was built with the intention of improving protection of user privacy through the management of the spatial and geographical information.