Efficient evaluation of range queries over spatial data by clients

The notion of Geometrically Searchable Encryption, and anticipated an effectual scheme, named FastGeo, to keep the privacy of clients’ spatial datasets kept and enquired at a public server. With FastGeo, which is a novel two-level search for scrambled spatial data, an honest-but-curious server can proficiently complete geometric range queries, and suitably return data points that are private a geometric range to a client without culture penetrating data points or this cloistered query. FastGeo wires arbitrary geometric areas, accomplishes sub linear search time, and qualifies go-ahead updates over encrypted spatial datasets. Our outline is provably sheltered, and our new results on real-world spatial datasets in cloud platform determine that FastGeo can enhance search time over 100 times

A STATISTICAL SEARCH ON CONVERTED GEOGRAPHICAL DATA

More specifically, you can expand and encrypt past searches, especially dealing with system comparisons or encrypting the save command, to allow the parallel rectangle range to explore spatial data. An information analyst can study the ability of social achievement according to the site's record for countless users by evaluating multiple forms of circular domain queries. Although most search-based file encryption systems focus on common SQL queries, for example, keyword queries and logical queries, some investigations have specifically investigated geometric range via encrypted spatial data. Our main contributions are the fact that our design is indeed a general approach, which can support several types of engineering term queries. None of these previous works have been studied in special engineering queries that are expressed as triangles or rectangles that are not parallel to the axis. With the rapid development of social systems, location-based services and travel on a laptop, the amount of data people creates each day continues to grow exponentially. It is no longer easy or perhaps profitable for companies to maintain a large amount of data in their area. More importantly, the general approach still does not exist, which can flexibly and securely support various types of geometric field queries on encrypted spatial data, regardless of their specific geometric shapes. Our design has great potential for use and is implemented in extensive applications, for example, location-based services and spatial databases, requiring the use of confidential spatial data based on a strong privacy guarantee

Lower Bounds for Oblivious Near-Neighbor Search

We prove an $\Omega(d \lg n/ (\lg\lg n)^2)$ lower bound on the dynamic cell-probe complexity of statistically $\mathit{oblivious}$ approximate-near-neighbor search ($\mathsf{ANN}$) over the $d$-dimensional Hamming cube. For the natural setting of $d = \Theta(\log n)$, our result implies an $\tilde{\Omega}(\lg^2 n)$ lower bound, which is a quadratic improvement over the highest (non-oblivious) cell-probe lower bound for $\mathsf{ANN}$. This is the first super-logarithmic $\mathit{unconditional}$ lower bound for $\mathsf{ANN}$ against general (non black-box) data structures. We also show that any oblivious $\mathit{static}$ data structure for decomposable search problems (like $\mathsf{ANN}$) can be obliviously dynamized with $O(\log n)$ overhead in update and query time, strengthening a classic result of Bentley and Saxe (Algorithmica, 1980).Comment: 28 page

Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation

With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the RASP data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201

Preserving Trustworthiness and Confidentiality for Online Multimedia

Technology advancements in areas of mobile computing, social networks, and cloud computing have rapidly changed the way we communicate and interact. The wide adoption of media-oriented mobile devices such as smartphones and tablets enables people to capture information in various media formats, and offers them a rich platform for media consumption. The proliferation of online services and social networks makes it possible to store personal multimedia collection online and share them with family and friends anytime anywhere. Considering the increasing impact of digital multimedia and the trend of cloud computing, this dissertation explores the problem of how to evaluate trustworthiness and preserve confidentiality of online multimedia data. The dissertation consists of two parts. The first part examines the problem of evaluating trustworthiness of multimedia data distributed online. Given the digital nature of multimedia data, editing and tampering of the multimedia content becomes very easy. Therefore, it is important to analyze and reveal the processing history of a multimedia document in order to evaluate its trustworthiness. We propose a new forensic technique called ``Forensic Hash", which draws synergy between two related research areas of image hashing and non-reference multimedia forensics. A forensic hash is a compact signature capturing important information from the original multimedia document to assist forensic analysis and reveal processing history of a multimedia document under question. Our proposed technique is shown to have the advantage of being compact and offering efficient and accurate analysis to forensic questions that cannot be easily answered by convention forensic techniques. The answers that we obtain from the forensic hash provide valuable information on the trustworthiness of online multimedia data. The second part of this dissertation addresses the confidentiality issue of multimedia data stored with online services. The emerging cloud computing paradigm makes it attractive to store private multimedia data online for easy access and sharing. However, the potential of cloud services cannot be fully reached unless the issue of how to preserve confidentiality of sensitive data stored in the cloud is addressed. In this dissertation, we explore techniques that enable confidentiality-preserving search of encrypted multimedia, which can play a critical role in secure online multimedia services. Techniques from image processing, information retrieval, and cryptography are jointly and strategically applied to allow efficient rank-ordered search over encrypted multimedia database and at the same time preserve data confidentiality against malicious intruders and service providers. We demonstrate high efficiency and accuracy of the proposed techniques and provide a quantitative comparative study with conventional techniques based on heavy-weight cryptography primitives