In this paper, we propose new location privacy preserving schemes for database-driven cognitive radio networks that protect secondary users’ (SUs) location privacy while allowing them to learn spectrum availability in their vicinity. Our schemes harness probabilistic set membership data structures to exploit the structured nature of spectrum databases (DBs) and SUs’ queries. This enables us to create a compact representation of DB that could be queried by SUs without having to share their location with DB, thus guaranteeing their location privacy. Our proposed schemes offer different cost-performance characteristics. Our first scheme relies on a simple yet powerful two-party protocol that achieves unconditional security with a plausible communication overhead by making DB send a compacted version of its content to SU which needs only to query this data structure to learn spectrum availability. Our second scheme achieves significantly lower communication and computation overhead for SUs, but requires an additional architectural entity which receives the compacted version of the database and fetches the spectrum availability information in lieu of SUs to alleviate the overhead on the latter. We show that our schemes are secure, and also demonstrate that they offer significant advantages over existing alternatives for various performance and or security metrics.
Protecting s' location privacy in database-driven s is a very challenging task, since s are required to provide their physical locations to in order for them to be able to learn about spectrum opportunities in their vicinities. Recently developed techniques mostly adopt either the k-anonymity @cite_25 , Private Information Retrieval ( ) @cite_41 , or differential privacy @cite_3 concepts. However, direct adaptation of such concepts yield either insecure or extremely costly results. For instance, k-anonymity guarantees that 's location is indistinguishable among a set of @math points, which could be achieved through the use of dummy locations by generating @math properly selected dummy points, and performing @math queries to using both the real and dummy locations. For example, @cite_18 rely on this concept to make each query by sending a square cloak region that includes its actual location. Their approach makes a tradeoff between providing high location privacy and maximizing some utility, which makes it suffer from the fact that achieving a high location privacy level results in a decrease in spectrum utility.
@cite_41 @cite_18 @cite_25 @cite_3
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Publicly accessible databases are an indispensable resource for retrieving up-to-date information. But they also pose a significant risk to the privacy of the user, since a curious database operator can follow the user's queries and infer what the user is after. Indeed, in cases where the users' intentions are to be kept secret, users are often cautious about accessing the database. It can be shown that when accessing a single database, to completely guarantee the privacy of the user, the whole database should be down-loaded; namely n bits should be communicated (where n is the number of bits in the database). In this work, we investigate whether by replicating the database, more efficient solutions to the private retrieval problem can be obtained. We describe schemes that enable a user to access k replicated copies of a database ( k ≥2) and privately retrieve information stored in the database. This means that each individual server (holding a replicated copy of the database) gets no information on the identity of the item retrieved by the user. Our schemes use the replication to gain substantial saving. In particular, we present a two-server scheme with communication complexity O(n 1 3 ). Database-driven Cognitive Radio Network (CRN) has been proposed to replace the requirement of spectrum sensing of terminal devices so that the operation of users is simplified. However, location privacy issues introduce a big challenge for securing database-driven CRN due to spectrum availability information. The existing works consider either PU or SU's location privacy while not the both. In this study, we identify a unified attack framework in which a curious user could infer a target's location based on the spectrum availability utilization information. Further, we propose a location privacy protection mechanism, which allows both SU and PU to protect their location privacy by adopting a series of countermeasures. The location privacy and spectrum utility are the trade-off. In the countermeasures of location privacy preserving spectrum query process, both SU and database aim to maximize the location privacy with constraints of spectrum utility. Thus, they can obtain higher location privacy level with sacrifice of spectrum utility as long as the spectrum utility meets the requirements. We evaluate the unified attack and defence approaches based on simulation and demonstrate the effectiveness of the proposed location privacy preserving approaches. Advances in sensing and tracking technology enable location-based applications but they also create significant privacy risks. Anonymity can provide a high degree of privacy, save service users from dealing with service providers’ privacy policies, and reduce the service providers’ requirements for safeguarding private information. However, guaranteeing anonymous usage of location-based services requires that the precise location information transmitted by a user cannot be easily used to re-identify the subject. This paper presents a middleware architecture and algorithms that can be used by a centralized location broker service. The adaptive algorithms adjust the resolution of location information along spatial or temporal dimensions to meet specified anonymity constraints based on the entities who may be using location services within a given area. Using a model based on automotive traffic counts and cartographic material, we estimate the realistically expected spatial resolution for different anonymity constraints. The median resolution generated by our algorithms is 125 meters. Thus, anonymous location-based requests for urban areas would have the same accuracy currently needed for E-911 services; this would provide sufficient resolution for wayfinding, automated bus routing services and similar location-dependent services. Over the past five years a new approach to privacy-preserving data analysis has born fruit [13, 18, 7, 19, 5, 37, 35, 8, 32]. This approach differs from much (but not all!) of the related literature in the statistics, databases, theory, and cryptography communities, in that a formal and ad omnia privacy guarantee is defined, and the data analysis techniques presented are rigorously proved to satisfy the guarantee. The key privacy guarantee that has emerged is differential privacy. Roughly speaking, this ensures that (almost, and quantifiably) no risk is incurred by joining a statistical database. In this survey, we recall the definition of differential privacy and two basic techniques for achieving it. We then show some interesting applications of these techniques, presenting algorithms for three specific tasks and three general results on differentially private learning.