Social networking services are increasingly accessed through mobile devices. This trend has prompted services such as Facebook and Google+ to incorporate location as a de facto feature of user interaction. At the same time, services based on location such as Foursquare and Shopkick are also growing as smartphone market penetration increases. In fact, this growth is happening despite concerns (growing at a similar pace) about security and third-party use of private location information (e.g., for advertising). Nevertheless, service providers have been unwilling to build truly private systems in which they do not have access to location information. In this paper, we describe an architecture and a trial implementation of a privacy-preserving location sharing system called Albatross. The system protects location information from the service provider and yet enables fine-grained location-sharing. One main feature of the system is to protect an individual's social network structure. The pattern of location sharing preferences towards contacts can reveal this structure without any knowledge of the locations themselves. Albatross protects locations sharing preferences through protocol unification and masking. Albatross has been implemented as a standalone solution, but the technology can also be integrated into location-based services to enhance privacy.
Existing production location sharing systems with a trusted server include Foursquare @cite_24 , Google Latitude @cite_5 , Apple's Find My Friends @cite_20 , and Glympse @cite_9 . There has been work on location sharing systems that enhance privacy through an untrusted server. The work by @cite_0 concentrates on a particular type of location sharing, the nearby'' sharing granularity. We aim for a more complete system that includes different granularities of location sharing and address the issue of how to hide these granularities from the server. @cite_14 , @cite_17 @cite_2 , and @cite_28 also focus on the nearby'' sharing granularity. Private versions of other types of social networking systems with an untrusted server have also been studied. @cite_21 studied micro-blogs, specifically Twitter. Their system is analogous to ours in that they attempt to provide the expected core micro-blogging services, and yet hide tweets, hashtags, and followers from the server. @cite_7 study presence systems, in which a user's presence status ( in building'', in office'', has visitor'', etc.) is hidden from the server. There is a large body of work studying recommender systems with untrusted servers; early work in this area was performed by Canny @cite_3 .
@cite_14 @cite_7 @cite_28 @cite_9 @cite_21 @cite_3 @cite_24 @cite_0 @cite_2 @cite_5 @cite_20 @cite_17
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The ubiquity of mobile devices has brought forth the concept of participatory sensing, whereby ordinary citizens can now contribute and share information from the urban environment. However, such applications introduce a key research challenge: preserving the privacy of the individuals contributing data. In this paper, we study two different privacy concepts, k-anonymity and l-diversity, and demonstrate how their privacy models can be applied to protect users' spatial and temporal privacy in the context of participatory sensing. The first part of the paper focuses on schemes implementing k-anonymity. We propose the use of microaggregation, a technique used for facilitating disclosure control in databases, as an alternate to tessellation, which is the current state-of-the-art for location privacy in participatory sensing applications. We conduct a comparative study of the two techniques and demonstrate that each has its advantage in certain mutually exclusive situations. We then propose the Hybrid Variable size Maximum Distance to Average Vector (Hybrid-VMDAV) algorithm, which combines the positive aspects of microaggregation and tessellation. The second part of the paper addresses the limitations of the k-anonymity privacy model. We employ the principle of l-diversity and propose an l-diverse version of VMDAV (LD-VMDAV) as an improvement. In particular, LD-VMDAV is robust in situations where an adversary may have gained partial knowledge about certain attributes of the victim. We evaluate the performances of our proposed techniques using real-world traces. Our results show that Hybrid-VMDAV improves the percentage of positive identifications made by an application server by up to 100 and decreases the amount of information loss by about 40 . We empirically show that LD-VMDAV always outperforms its k-anonymity counterpart. In particular, it improves the ability of the applications to accurately interpret the anonymized location and time included in user reports. Our studies also confirm that perturbing the true locations of the users with random Gaussian noise can provide an extra layer of protection, while causing little impact on the application performance. Mobile devices with geo-positioning capabilities (e.g., GPS) enable users to access information that is relevant to their present location. Users are interested in querying about points of interest (POI) in their physical proximity, such as restaurants, cafes, ongoing events, etc. Entities specialized in various areas of interest (e.g., certain niche directions in arts, entertainment, travel) gather large amounts of geo-tagged data that appeal to subscribed users. Such data may be sensitive due to their contents. Furthermore, keeping such information up-to-date and relevant to the users is not an easy task, so the owners of such data sets will make the data accessible only to paying customers. Users send their current location as the query parameter, and wish to receive as result the nearest POIs, i.e., nearest-neighbors (NNs). But typical data owners do not have the technical means to support processing queries on a large scale, so they outsource data storage and querying to a cloud service provider. Many such cloud providers exist who offer powerful storage and computational infrastructures at low cost. However, cloud providers are not fully trusted, and typically behave in an honest-but-curious fashion. Specifically, they follow the protocol to answer queries correctly, but they also collect the locations of the POIs and the subscribers for other purposes. Leakage of POI locations can lead to privacy breaches as well as financial losses to the data owners, for whom the POI data set is an important source of revenue. Disclosure of user locations leads to privacy violations and may deter subscribers from using the service altogether. In this paper, we propose a family of techniques that allow processing of NN queries in an untrusted outsourced environment, while at the same time protecting both the POI and querying users’ positions. Our techniques rely on mutable order preserving encoding (mOPE), the only secure order-preserving encryption method known to-date. We also provide performance optimizations to decrease the computational cost inherent to processing on encrypted data, and we consider the case of incrementally updating data sets. We present an extensive performance evaluation of our techniques to illustrate their viability in practice. Conventional mobile social services such as Loopt and Google Latitude rely on two classes of trusted relationships: participants trust a centralized server to manage their location information and trust between users is based on existing social relationships. Unfortunately, these assumptions are not secure or general enough for many mobile social scenarios: centralized servers cannot always be relied upon to preserve data confidentiality, and users may want to use mobile social services to establish new relationships. To address these shortcomings, this paper describes SMILE, a privacy-preserving "missed-connections" service in which the service provider is untrusted and users are not assumed to have pre-established social relationships with each other. At a high-level, SMILE uses short-range wireless communication and standard cryptographic primitives to mimic the behavior of users in existing missed-connections services such as Craigslist: trust is founded solely on anonymous users' ability to prove to each other that they shared an encounter in the past. We have evaluated SMILE using protocol analysis, an informal study of Craigslist usage, and experiments with a prototype implementation and found it to be both privacy-preserving and feasible. Abstract With the proliferation of smart grids, traditional utilities are struggling to handle the increasing amount of metering data. Outsourcing the metering data to heterogeneous distributed systems has the potential to provide efficient data access and processing. In an untrusted heterogeneous distributed system environment, employing data encryption prior to outsourcing can be an effective way to preserve user privacy. However, how to efficiently query encrypted multidimensional metering data stored in an untrusted heterogeneous distributed system environment remains a research challenge. In this paper, we propose a high performance and privacy-preserving query (P2Q) scheme over encrypted multidimensional big metering data to address this challenge. In the proposed scheme, encrypted metering data are stored in the server of an untrusted heterogeneous distributed system environment. A Locality Sensitive Hashing (LSH) based similarity search approach is then used to realize the similarity query. To demonstrate utility of the proposed LSH-based search approach, we implement a prototype using MapReduce for the Hadoop distributed environment. More specifically, for a given query, the proxy server will return K top similar data object identifiers. An enhanced Ciphertext-Policy Attribute-based Encryption (CP-ABE) policy is then used to control access to the search results. Therefore, only the requester with an authorized query attribute can obtain the correct secret keys to retrieve the metering data. We then prove that the P2Q scheme achieves data confidentiality and preserves the data owner’s privacy in a semi-trusted cloud. In addition, our evaluations demonstrate that the P2Q scheme can significantly reduce response time and provide high search efficiency without compromising on search quality (i.e. suitable for multidimensional big data search in heterogeneous distributed system, such as cloud storage system).