Fig 15 - uploaded by Panos Kalnis
Content may be subject to copyright.
Source publication
![Fig. 2 Approximate NN PIR protocol from [5]](profile/Panos-Kalnis/publication/220387642/figure/fig2/AS:394139167412227@1470981508251/Approximate-NN-PIR-protocol-from-5_Q320.jpg)
![Fig. 3 Mapping Voronoi cells to grid in the exact NN PIR protocol from [5]](profile/Panos-Kalnis/publication/220387642/figure/fig14/AS:668741033529345@1536451695476/Mapping-Voronoi-cells-to-grid-in-the-exact-NN-PIR-protocol-from-5_Q320.jpg)
Mobile devices with global positioning capabilities allow users to retrieve points of interest (POI) in their proximity. To
protect user privacy, it is important not to disclose exact user coordinates to un-trusted entities that provide location-based
services. Currently, there are two main approaches to protect the location privacy of users: (i) h...
Similar publications
The sharing of association rules has been proven beneficial in business collaboration, but requires privacy safeguards. One may decide to disclose only part of the knowledge and conceal strategic patterns called sensitive rules. The challenge here is how to protect the sensitive rules without losing the benefit of mining. To address this problem, w...
Citations
... The principle of spatial transformation for location privacypreserving is to convert the actual location of the user from the conventional data space to another. Ghinita et al. [24] designed an algorithm relied on the additive homomorphic Paillier encryption scheme to accomplish the strong privacy protection. Um et al. [25] proposed an efficient scheme based on Hilbert curve which reduced the computation cost of encryption. ...
k-nearest neighbor (k-NN) query is widely applied to various networks, such as mobile Internet, peer-to-peer (P2P) network, urban road networks, and so on. The location-based service in the outsourced environment has become a research hotspot with the rise of cloud computing. Meanwhile, various privacy issues have been increasingly prominent. We propose an efficient privacy-preserving query protocol to accomplish the k-nearest neighbor (k-NN) query processing on outsourced data. We adopt the Moore curve to transform the spatial data into one-dimensional sequence and utilize the AES to encrypt the original data. According to the cryptographic transformation, the proposed protocol can minimize the communication overhead to achieve efficient k-NN query while protecting the spatial data and location privacy. Furthermore, the proposed efficient scheme offers considerable performance with privacy preservation. Experiments show that the proposed scheme achieves high accuracy and efficiency while preserving the data and location privacy when compared with the existing related approach.
... However, owing to the route uncertainty of all involved vehicles, it's hard to construct successive cloaking regions maintaining k-anonymity in mobile IoT [9], i.e., successive cloaking regions should retain most vehicles in the initial cloaking region, such that attackers are uncertain to find the actual vehicle who initiates the query. And most trusted servers confront a service bottleneck at the demands of cloaking region construction [10,15]. Wang et al have discussed the balance between privacy preservation and data integrity in various IoT and the trust issues of data collection [16][17][18][19][20][21][22][23]. ...
... An existing effective protection for user interest privacy is private information retrieval (PIR) [3,4,15,[26][27][28]. By means of PIR, vehicles are capable to calculate desired query results after obtaining a response cryptographic data block from SP without revealing any private information. ...
... By means of PIR, vehicles are capable to calculate desired query results after obtaining a response cryptographic data block from SP without revealing any private information. However, most existing PIR schemes are devised on strong cryptology and inapplicable to mobile IoT due to expensive processing overhead from frequent service requests with constantly updating sensing data [15,26]. Moreover, most PIR schemes have to utilize a trusted third party [27,28] between a vehicle and an SP to preprocess the database, which increases the risk of data exposure. ...
The emergence of mobile Internet of Things (IoT) has brought much more convenience to our daily life than ever before, modern intelligent vehicles are usually equipped with various mobile IoT components or devices. However, when we are enjoying the convenience from the rapid development of mobile IoT services, our privacy can be misused by attackers in an easier way. In this paper, we devise a query scheme with intelligent vehicle privacy guarantees, which enables a vehicle to acquire accurate query service from the service provider (SP) without providing its explicit private information, such as location or query interest privacy. And more than that, we introduce network coding for the first time to make our scheme applicable to a more sophisticated top-K query by multiple vehicles cooperation in mobile IoT. Furthermore, we also consider the unnecessary reveal issue of service data at SP side since the data is its asset. Performance analysis and experiments verify the validity of our scheme and demonstrate a better accuracy and efficiency compared with existing solutions in mobile IoT scenario.
... Other approaches may assume the use of a dedicated cryptographic processor [14], or a trusted third party [11] which may not be available. In [2,6], methods relying on homomorphic encryption are presented, which, however, exhibit high overhead [10,12]. In contrast to these approaches, we do not focus on only hiding a user's location when querying for nearby POIs, but we aim at locating nearby friends, who also have cloaked locations. ...
Nowadays, social networks are a part of everyday life. Almost everyone possessing a computing device, even a mobile one, such as a smartphone or tablet, has access to these networks. Interacting with them often requires sharing information both with the other users of the social network and with the social network itself. One of the cases that information has to be exchanged is by using services such as Facebook’s “Nearby Friends,” where a user has to share her location in order to locate her nearby friends, an action that undermines the user’s privacy. Current privacy preservation mechanisms only consider range nearest neighbor queries for nearest friend searches, limiting private friend discovery within a user’s predefined range. In this paper, we take private friend searches a step further, by presenting Two-Hop Privacy, a novel method for discovering a user’s nearest friends within arbitrary distance, not being constrained by range boundaries, in sublinear time, preserving, at the same time, the location privacy of all involved users. This is achieved by exploiting positional information of publicly available datasets of points of interest together with a randomized selection algorithm. Two-Hop Privacy is fast, requiring less than 9 ms to locate the 64 nearest neighbors between 5000 interconnected users, and capable of achieving accuracy up to 100%.
... Actually, their security depends on the size of anonymity set and thus they are vulnerable to inference attacks [19,26,39]. PIR based schemes [16,30] can also be applied to our setting. Riders can run ride-matching locally and retrieve needed data from the server using PIR technique during the computation. ...
Promising unprecedented convenience, Online Ride Hailing (ORH) service such as Uber and Didi has gained increasing popularity. Different from traditional taxi service, this new on-demand transportation service allows users to request rides from the online service providers at the touch of their fingers. Despite such great convenience, existing ORH systems require the users to expose their locations when requesting rides - a severe privacy issue in the face of untrusted or compromised service providers. In this paper, we propose a private yet efficient ride request scheme, allowing the user to enjoy public ORH service without sacrificing privacy. Unlike previous works, we consider a more practical setting where the information about the drivers and road networks is public. This poses an open challenge to achieve strong security and high efficiency for the secure ORH service. Our main leverage in addressing this problem is hardware-enforced Trusted Execution Environment, in particular Intel SGX enclave. However, the use of secure enclave does not lead to an immediate solution due to the hardware's inherent resource constraint and security limitation. To tackle the limited enclave space, we first design an efficient ride-matching algorithm utilizing hub-based labeling technique, which avoids loading massive road network data into enclave during online processing. To defend against side-channel attacks, we take the next step to make the ride-matching algorithm data-oblivious, by augmenting it with oblivious label access and oblivious distance computation. The proposed solution provides high efficiency of real-time response and strong security guarantee of data-obliviousness. We implement a prototype system of the proposed scheme and thoroughly evaluate it from both theoretical and experimental aspects. The results show that the proposed scheme permits accurate and real-time ride-matching with provable security.
... Privacy III: LSP's data privacy against users. LSP's database is the valuable and protected business asset [8,12,33] and the principle of least privilege [29] applies where no more information than the requested query answer should be returned to the users. Another reason for this privacy is the pay-per-result model [8,33] where the users who pay for k results should not receive more than k results. ...
... Although many solutions, such as [1,3,12,13,17,21,26,27,30,34,36,37], are proposed for the single user query (i.e., n = 1), only a few works addressed the group query (i.e., n > 1) [2,14] but none of them achieves all four privacy concerns. Most existing work achieved Privacy I through returning candidate answers (e.g., [3,13,14,17,21,26,30]) or approximate answers (e.g., [1,2,34,37]). ...
... However, returning candidate answers not only increases the communication cost but also violates Privacy III, while returning approximate answers degrades the answer utility as well as violates Privacy II since LSP knows the query answer that users obtained. [12,27,36] achieve Privacy I-III in the single user query case by heavily relying on pre-computing the query answers for all queries. These approaches are not applicable to the group query where the number of possible queries is large. ...
Group k-nearest neighbor (kGNN) search allows a group of n mobile users to jointly retrieve k points from a location-based service provider (LSP) that minimizes the aggregate distance to them. We identify four protection objectives in the privacy preserving kGNN search: (i) every user's location should be protected from LSP; (ii) the group's query and the query answer should be protected from LSP; (iii) LSP's private database information should be protected from users, i.e., the users cannot learn more information beyond the answer they requested; (iv) every user's location should be protected from the other users in the group. We propose the first approach to meet the four privacy goals in the kGNN query. Our approach provides an accurate query answer and does not rely on heavy pre-computation on LSP like previous works. Our approach considers the most hostile environment that any n − 1 users in the query group may collude to infer the location of the remaining user. Though we consider kGNN, the proposed privacy preserving approach can be easily adopted to any group query because it treats the query answering (i.e., kGNN) as a black box. Theoretical and experimental analysis suggest that our approach is highly efficient in both user computation and communication while incurring some reasonable overhead on LSP.
... The principle of spatial transformation for protecting location privacy is to convert the location information from conventional data space to another. The scheme [42] utilizes homomorphic encryption to accomplish data interaction between users and servers. Although it achieves strong privacy protection, it is difficult to be adapted to the application environment of continuous queries and real-time responses with extremely expensive computation cost. ...
With the development of mobile communication technology, location-based services (LBS) are booming prosperously. Meanwhile privacy protection has become the main obstacle for the further development of LBS. The k -nearest neighbor ( k -NN) search is one of the most common types of LBS. In this paper, we propose an efficient private circular query protocol (EPCQP) with high accuracy rate and low computation and communication cost. We adopt the Moore curve to convert two-dimensional spatial data into one-dimensional sequence and encrypt the points of interest (POIs) information with the Brakerski-Gentry-Vaikuntanathan homomorphic encryption scheme for privacy-preserving. The proposed scheme performs the secret circular shift of the encrypted POIs information to hide the location of the user without a trusted third party. To reduce the computation and communication cost, we dynamically divide the table of the POIs information according to the value of k . Experiments show that the proposed scheme provides high accuracy query results while maintaining low computation and communication cost.
... However, the energy consumption is quite high for smart phones due to the high communication cost of the k-Anonymity and cloaking techniques. On the contrary, the homomorphic encryption technique [11,12] is able to query encrypted data with low communication cost, and ensures a negligible probability of being identified. However, their computation cost is too high to be executed for smart phones. ...
With the advance of wireless communication techniques and the popularity of embedded devices, Location-Based Service (LBS) has gained a great attention in the Internet-of-Things (IoT) recently. For instance, in smart transportation, reporting all the drivers' location in a regular frequency is conducive to sense urban traffic congestion. However, it also posts a serious threat to drivers' privacy because of the exposure to the real-time location. To alleviate this problem, we propose a Secure and Efficient Location-based Service (SELS) scheme for smart transportation in this paper. In the SELS scheme, drivers first utilize smart phones to encrypt their location-based data and then outsource them to a cloud center incessantly. Then they have the ability to call the cloud center to compute the ciphertext of LBS queries, by using a secure homomorphic encryption scheme and group signature scheme. After decrypting the ciphertext, they obtain accurate results of the queries without sacrificing the location privacy. Furthermore, the SELS scheme also supports multi-dimension data by employing a weighted distance algorithm. Finally, the security of the SELS scheme is proved and the cost of computation and communication is analyzed, in order to prove the security and efficiency of the scheme.
... PIR [10] allows a user to retrieve a record from a database server without revealing which record he is retrieving. The PIR-based protocols [4,12] are proposed for mainly POI queries. The approaches proposed by [4,5] are based on homomorphic encryption while the technique of Paulet et al. [12] is based on oblivious transfer. ...
... The PIR-based protocols [4,12] are proposed for mainly POI queries. The approaches proposed by [4,5] are based on homomorphic encryption while the technique of Paulet et al. [12] is based on oblivious transfer. The main similarity between our proposed work and existing techniques is preserving users' privacy in location based services and the key difference is that we focus on social application which is generating location recommendation while preserving users' privacy. ...
With the rapid development of location based social networks (LBSN) and location based services (LBS), the location recommendation to users has gained much attentions. A traditional location recommendation scheme may use any of the following information to generate a location recommendation: users’ check-in frequencies on different locations, their distance of other locations from any point of interest (POI), time of visiting different locations, social influence or interests on those locations which are visited by friends and so on. Depending on different contexts and tastes, results of recommending new location may vary. Again the users might have specific preferences of context to find the most suitable locations for him. However, these contextual information and preferences related to users are personal and an user usually does not want to reveal these information to any third party which are the main source of information to generate a recommendation. Revealing these information may cause to misuse or expose the data to third parties which is clearly breaching privacy of users. In this circumstances, it is essential to hide users’ check-in history in different locations from service providers, and get advantages of the server’s processing power to generate user personalized location recommendations. To address these challenges we present a cryptographic framework to preserve users’ privacy and simultaneously generating location recommendations for users. We also incorporate users’ friendship network along with the location preferences and show that users are able to choose their friends’ preferences on different locations to influence the recommendation results without revealing any information. The security and performance analysis show that the protocol is secure as well as practical.
... However, in order to obtain these services, the user has to submit to the data owner of the current location or source/destination address, and this process may lead to breach in privacy. To this end, a series of methods were provided, but most of existing methods mainly dedicate to preserve the location privacy of the user queries for at least k users in the vicinity, such as the k-nearest neighbor (kNN) query, some existing solutions of this type include [1,2]. In contrast, preservation for the shortest path has seldom been addressed. ...
With the prosperity of location-based services, shortest path query became one of the most common services, and provided convenience for finding the best way to arrive at the destination of users. However, in order to obtain the service, users had to submit the current locations to a data owner, and this process may pose privacy threats to users. More seriously, if the data owner outsourcing the query data to a cloud server for calculating the shortest distance, users’ privacy may be leaked even further. To cope with the problem of outsourcing computation, based the homomorphic encryption and secure multiparty computation, this manuscript presents a framework to reduce the likelihood of private information leakage, and privacy preservation of both the user, and the data owner. In this framework, two different conditions were considered, and the shortest path was computed with or without obstruction on the road. Thereafter, two protocols called “query with obstruction” and “query without obstruction” are presented.
... Private Information Retrieval [13]: This technique allows a user to retrieve a record from a database server without revealing which record he is retrieving. PIR-based protocols [1], [9], [12], [13] are proposed for POI queries and composed of two stages. In the first stage, the user privately determines the index of his location through the service provider without disclosing his coordinates to it. ...
... In addition, trusted hardware was employed to perform PIR for LBS queries [8]. Their technique is built on hardware-aided PIR [9], which relies on a trusted third party to set the secret key and the permutation of the database. Like LBS queries based on access control, mix zone and k-anonymity, this technique is vulnerable to misbehavior of the third party. ...
... The product of a cipher text with a plaintext raising g will decrypt to the sum of their corresponding plaintexts [9] ( ( 1 , 1 ). 2 2 ) = 1 + 2 ...
Searching query with user location privacy is so difficult because the location of a query may reveal sensitive information about the mobile user. In this paper, we study k nearest neighbor (kNN) queries where the mobile user queries the location-based service (LBS) provider about k nearest points of interest (POIs) on the basis of his current location. We propose a basic solution and a generic solution for the mobile user to preserve his location and query privacy in kNN queries. The proposed solutions are mainly built on the Enhanced Homomorphic cryptosystem and can provide both location and query privacy. Without the help of base station, the mobile user and LBS provider can search query and provide the user location as privacy. To preserve query privacy, our basic solution allows the mobile user to retrieve one type of POIs, for example, approximate k nearest hospital, without revealing to the LBS provider what type of points is retrieved. By this proposed algorithm, maintain privacy between the user searching query and user location. © 2017, Institute of Advanced Scientific Research, Inc. All rights reserved.
