Spatial cloaking is a privacy mechanism that is used to satisfy specific privacy requirements by blurring users' exact locations into cloaked regions. This technique is usually integrated into applications in various environments to minimize the disclosure of private information when users request location-based service. Since the database server does not receive the accurate location information, a set including the satisfying solution would be sent back to the user. General privacy requirements include K-anonymity, maximum area, and minimum area.
Background With the emergence and popularity of location-based services, people are getting more personalized services, such as getting the names and locations of nearby restaurants and gas stations. Receiving these services requires users to send their positions either directly or indirectly to the service provider. A user's location information could be shared more than 5000 times in two weeks. Therefore, this convenience also exposes users' privacy to certain risks, since the attackers may illegally identify the users' locations and even further exploit their personal information. Continuously tracking users' location has not only been identified as a technical issue, but also a privacy concern as well. It has been realized that Quasi-identifiers, which refer to a set of information attributes, can be used to re-identify the user when linked with some external information. For example, the social security number could be used to identify a specific user by adversaries, and the combined disclosure of birth date, zip code, and gender can uniquely identify a user. Thus, multiple solutions have been proposed to preserve and enhance users' privacy when using location-based services. Among all the proposed mechanisms, spatial cloaking is one of those which has been widely accepted and revised, thus having been integrated into many practical applications.
Location privacy Location privacy is usually considered falling into the category of information privacy, though there is little consensus on the definition of location privacy. There are often three aspects of location information: identity, location (spatial information), and time (temporal information). Identity usually refers to a user's name, email address, or any characteristic which makes a user distinguishable. For example, Pokémon Go requires a consistent user identity, since users are required to log in. Spatial information is considered as the main approach to determine a location. Temporal information can be separated into real-time and non-real time and is usually described as a time stamp with a place. If a link is established between them, then the location privacy is considered violated. Accessing personal location data has been raised as a severe privacy concern, even with personal permission. Therefore, privacy-aware management of location information has been identified as an essential challenge, which is designed to provide privacy protection against abuse of location information. The overall idea of preserving location privacy is to introduce enough noise and quantization to reduce the chances of successful attacks. Spatial crowdsourcing uses devices that has GPS (global positioning system) and collects information. Data retrieved includes location data that can be used to analyze maps and local spatial characteristics. In recent years, researchers have been making a connection between social aspects and technological aspects regarding location information. For example, if co-location information is considered as the data which potential attackers would get and take into consideration, the location privacy is decreased by more than 60%. Also, by a constant report of a user's location information, a movement profile could be constructed for this specific user based on statistical analysis, and a large amount of information could be exploited and generated from this profile such as user's office location, medical records, financial status, and political views. Therefore, more and more researchers have taken account of the social influence in their algorithms, since this socially networked information is accessible to the public and might be used by potential attackers.
History In order to meet user's requirements for location privacy in the process of data transportation, researchers have been exploring and investigating models to address the disclosure of private information. The secure-multi-party model is constructed based on the idea of sharing accurate information among n parties. Each party has access to a particular segment of the precise information and at the same time being prevented from acquiring the other shares of the data. However, the computation problem is introduced in the process, since a large amount of data processing is required to satisfy the requirement. The minimal information sharing model is introduced to use cryptographic techniques to perform join and intersection operations. However, the inflexibility of this model to fit into other queries makes it hard to be satisfying to most practical applications. The untrusted third-party model is adopted in peer-to-peer environments. The most popular model right now is the trusted third-party model. Some of the practical applications have already adopted the idea of a trusted third party into their services to preserve privacy. For example, Anonymizer is integrated into various websites, which could give anonymous surfing service to its users. Also, when purchasing through PayPal, users are not required to provide their credit card information. Therefore, by introducing a trusted-third-party, users' private information is not directly exposed to the service providers.
Approaches for preserving location information The promising approach of preserving location privacy is to report data on users' behavior and at the same time protect identity and location privacy. Several methods have been investigated to enhance the performances of location-preserving techniques, such as location perturbation and the report of landmark objects.
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