A Practical Searchable Symmetric Encryption Scheme for Smart Grid Data

Outsourcing data storage to the remote cloud can be an economical solution to enhance data management in the smart grid ecosystem. To protect the privacy of data, the utility company may choose to encrypt the data before uploading them to the cloud. However, while encryption provides confidentiality to data, it also sacrifices the data owners' ability to query a special segment in their data. Searchable symmetric encryption is a technology that enables users to store documents in ciphertext form while keeping the functionality to search keywords in the documents. However, most state-of-the-art SSE algorithms are only focusing on general document storage, which may become unsuitable for smart grid applications. In this paper, we propose a simple, practical SSE scheme that aims to protect the privacy of data generated in the smart grid. Our scheme achieves high space complexity with small information disclosure that was acceptable for practical smart grid application. We also implement a prototype over the statistical data of advanced meter infrastructure to show the effectiveness of our approach

Balancing Security, Performance and Deployability in Encrypted Search

Encryption is an important tool for protecting data, especially data stored in the cloud. However, standard encryption techniques prevent efficient search. Searchable encryption attempts to solve this issue, protecting the data while still providing search functionality. Retaining the ability to search comes at a cost of security, performance and/or utility. An important practical aspect of utility is compatibility with legacy systems. Unfortunately, the efficient searchable encryption constructions that are compatible with these systems have been proven vulnerable to attack, even against weaker adversary models. The goal of this work is to address this security problem inherent with efficient, legacy compatible constructions. First, we present attacks on previous constructions that are compatible with legacy systems, demonstrating their vulnerability. Then we present two new searchable encryption constructions. The first, weakly randomized encryption, provides superior security to prior easily deployable constructions, while providing similar ease of deployment and query performance nearly identical to unencrypted databases. The second construction, EDDiES, provides much stronger security at the expense of a slight regression on performance. These constructions show that it is possible to achieve a better balance of security and performance with the utility constraints that come with deployment in legacy systems

GraphSE2^2: An Encrypted Graph Database for Privacy-Preserving Social Search

In this paper, we propose GraphSE$^2$, an encrypted graph database for online social network services to address massive data breaches. GraphSE$^2$ preserves the functionality of social search, a key enabler for quality social network services, where social search queries are conducted on a large-scale social graph and meanwhile perform set and computational operations on user-generated contents. To enable efficient privacy-preserving social search, GraphSE$^2$ provides an encrypted structural data model to facilitate parallel and encrypted graph data access. It is also designed to decompose complex social search queries into atomic operations and realise them via interchangeable protocols in a fast and scalable manner. We build GraphSE$^2$ with various queries supported in the Facebook graph search engine and implement a full-fledged prototype. Extensive evaluations on Azure Cloud demonstrate that GraphSE$^2$ is practical for querying a social graph with a million of users.Comment: This is the full version of our AsiaCCS paper "GraphSE$^2$: An Encrypted Graph Database for Privacy-Preserving Social Search". It includes the security proof of the proposed scheme. If you want to cite our work, please cite the conference version of i

Trade-offs in Private Search

Encrypted search -- performing queries on protected data -- is a well researched problem. However, existing solutions have inherent inefficiency that raises questions of practicality. Here, we step back from the goal of achieving maximal privacy guarantees in an encrypted search scenario to consider efficiency as a priority. We propose a privacy framework for search that allows tuning and optimization of the trade-offs between privacy and efficiency. As an instantiation of the privacy framework we introduce a tunable search system based on the SADS scheme and provide detailed measurements demonstrating the trade-offs of the constructed system. We also analyze other existing encrypted search schemes with respect to this framework. We further propose a protocol that addresses the challenge of document content retrieval in a search setting with relaxed privacy requirements

Towards a secure and efficient search over encrypted cloud data

Includes bibliographical references.2016 Summer.Cloud computing enables new types of services where the computational and network resources are available online through the Internet. One of the most popular services of cloud computing is data outsourcing. For reasons of cost and convenience, public as well as private organizations can now outsource their large amounts of data to the cloud and enjoy the benefits of remote storage and management. At the same time, confidentiality of remotely stored data on untrusted cloud server is a big concern. In order to reduce these concerns, sensitive data, such as, personal health records, emails, income tax and financial reports, are usually outsourced in encrypted form using well-known cryptographic techniques. Although encrypted data storage protects remote data from unauthorized access, it complicates some basic, yet essential data utilization services such as plaintext keyword search. A simple solution of downloading the data, decrypting and searching locally is clearly inefficient since storing data in the cloud is meaningless unless it can be easily searched and utilized. Thus, cloud services should enable efficient search on encrypted data to provide the benefits of a first-class cloud computing environment. This dissertation is concerned with developing novel searchable encryption techniques that allow the cloud server to perform multi-keyword ranked search as well as substring search incorporating position information. We present results that we have accomplished in this area, including a comprehensive evaluation of existing solutions and searchable encryption schemes for ranked search and substring position search

Survey Paper on Multi Keyword Similarity Search over Encrypted Cloud Data

The tremendous amount of data outsourced every day by individuals or each enterprises . It is impossible to manage or to store this complex data at individual level, as the chances of crash the system is more, and the system becomes the single point of failure.When we feel the need of storing the data in such a way that it can be accessed uninterruptedly, then there the cloud comes into picture to store the data with better flexibility and cost saving. As the data might be confidential or sensitive. Considering the privacy of the data over the cloud, for that searchable encryption can be used. At the time of retrieval of data, consider the multi-keyword search over outsourced cloud text data only as it can handle the exact keywork matching. Multi-keyword similarity search overcomes the problem of not finding any related documents on searching. while encrypting the data before storing it to the cloud will help to preserve the privacy of the files. Searchable encryption also enables searching without revealing any additional information. Using multi-keyword similarity search cloud returns the files containing more number of matches with user input keywords and similar keyworks. Finding the similarities between input keyword or similar keyword is done by edit distance metric algorithm. Final design to achieve the user privacy, and to speedup the search task. At cloud side Bloom Filter’s bit pattern is used to speedup and it is efficient in terms of the search time at the cloud side. This paper presents a review on various existing Similarity searching techniques

OS2: Oblivious similarity based searching for encrypted data outsourced to an untrusted domain

© 2017 Pervez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Public cloud storage services are becoming prevalent and myriad data sharing, archiving and collaborative services have emerged which harness the pay-as-you-go business model of public cloud. To ensure privacy and confidentiality often encrypted data is outsourced to such services, which further complicates the process of accessing relevant data by using search queries. Search over encrypted data schemes solve this problem by exploiting cryptographic primitives and secure indexing to identify outsourced data that satisfy the search criteria. Almost all of these schemes rely on exact matching between the encrypted data and search criteria. A few schemes which extend the notion of exact matching to similarity based search, lack realism as those schemes rely on trusted third parties or due to increase storage and computational complexity. In this paper we propose Oblivious Similarity based Search (OS2) for encrypted data. It enables authorized users to model their own encrypted search queries which are resilient to typographical errors. Unlike conventional methodologies, OS2 ranks the search results by using similarity measure offering a better search experience than exact matching. It utilizes encrypted bloom filter and probabilistic homomorphic encryption to enable authorized users to access relevant data without revealing results of search query evaluation process to the untrusted cloud service provider. Encrypted bloom filter based search enables OS2 to reduce search space to potentially relevant encrypted data avoiding unnecessary computation on public cloud. The efficacy of OS2 is evaluated on Google App Engine for various bloom filter lengths on different cloud configurations

Multi Keyword Similarity Search Over Encrypted Text Data on Cloud

The tremendous amount of data is being outsourced every day by individuals or enterprises . It is not feasible to manage or to store such a large data locally, due to the limited storage capacities, and the system becomes the single point of failure. the cloud comes into picture to store the data with better flexibility and cost saving. As the data might be confidential or sensitive, the data which user wants to store on the cloud can be private and it should not be leaked, for that purpose searchable encryption is be used, so that even if the file falls in wrong hands it will be safe. At the time of retrieval of data, the multi-keyword search over text data can only handle the exact keywork matching. Multi-keyword similarity search overcomes the problem of not finding any related documents on searching. while encrypting the data before storing it to the cloud will help to preserve the privacy of the files. Finding the similarities between input keyword or similar keyword is done by edit distance metric algorithm. Final design to achieve the user privacy, and to speedup the search task. At cloud side Bloom Inverted List is used to implement searching on index

Searchable Symmetric Encryption and its applications

In the age of personalized advertisement and online identity profiles, people’s personal information is worth more to corporations than ever. Storing data in the cloud is increasing in popularity due to bigger file sizes and people just storing more information digitally. The leading cloud storage providers require insight into what users store on their servers. This forces users to trust their cloud storage provider not to misuse their information. This opens the possibility that private information is sold to hackers or is made publicly available on the internet. However, the more realistic case is that the service provider sells or misuses your metadata for use in personalized advertisements or other, less apparent purposes. This thesis will explore Searchable Sym- metric Encryption (SSE) algorithms and how we can utilize them to make a more secure cloud storage serviceMasteroppgave i informatikkINF399MAMN-PROGMAMN-IN