Link Prediction is an important and well-studied problem for social networks. Given a snapshot of a graph, the link prediction problem predicts which new interactions between members are most likely to occur in the near future. As networks grow in size, data owners are forced to store the data in remote cloud servers which reveals sensitive information about the network. The graphs are therefore stored in encrypted form. We study the link prediction problem on encrypted graphs. To the best of our knowledge, this secure link prediction problem has not been studied before. We use the number of common neighbors for prediction. We present three algorithms for the secure link prediction problem. We design prototypes of the schemes and formally prove their security. We execute our algorithms in real-life datasets.
Chase and Kamara @cite_19 introduced the notion of graph encryption while they were presenting structured encryption as a generalization of searchable symmetric encryption (SSE) proposed by Song @cite_23 . They presented schemes for , and on labeled graph-structured data. In all of their proposed schemes, the graph was considered as an adjacency matrix and each entry was encrypted separately using symmetric key encryption. The main idea of their scheme, given a vertex and the corresponding key, the scheme could return adjacent vertices. However, complex query requires complex operation (like addition, subtraction, division etc.) on adjacent matrix which make the scheme unsuitable.
@cite_19 @cite_23
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Driven by the growing security demands of data outsourcing applications in sustainable smart cities, encrypting clients’ data has been widely accepted by academia and industry. Data encryptions should be done at the client side before outsourcing, because clouds and edges are not trusted. Therefore, how to properly encrypt data in a way that the encrypted and remotely stored data can still be queried has become a challenging issue. Though keyword searches over encrypted textual data have been extensively studied, approaches for encrypting graph-structured data with support for answering graph queries are still lacking in the literature. In this paper, we specially investigate graph encryption method for an important graph query type, called top-k Nearest Keyword (kNK) searches. We design several indexes to store necessary information for answering queries and guarantee that private information about the graph such as vertex identifiers, keywords and edges are encrypted or excluded. Security and efficiency of our graph encryption scheme are demonstrated by theoretical proofs and experiments on real-world datasets, respectively. We propose graph encryption schemes that efficiently support approximate shortest distance queries on large-scale encrypted graphs. Shortest distance queries are one of the most fundamental graph operations and have a wide range of applications. Using such graph encryption schemes, a client can outsource large-scale privacy-sensitive graphs to an untrusted server without losing the ability to query it. Other applications include encrypted graph databases and controlled disclosure systems. We propose GRECS (stands for GRaph EnCryption for approximate Shortest distance queries) which includes three oracle encryption schemes that are provably secure against any semi-honest server. Our first construction makes use of only symmetric-key operations, resulting in a computationally-efficient construction. Our second scheme makes use of somewhat-homomorphic encryption and is less computationally-efficient but achieves optimal communication complexity (i.e. uses a minimal amount of bandwidth). Finally, our third scheme is both computationally-efficient and achieves optimal communication complexity at the cost of a small amount of additional leakage. We implemented and evaluated the efficiency of our constructions experimentally. The experiments demonstrate that our schemes are efficient and can be applied to graphs that scale up to 1.6 million nodes and 11 million edges. We consider the problem of encrypting structured data (e.g., a web graph or a social network) in such a way that it can be efficiently and privately queried. For this purpose, we introduce the notion of structured encryption which generalizes previous work on symmetric searchable encryption (SSE) to the setting of arbitrarily-structured data. We survey the notion of provably secure searchable encryption (SE) by giving a complete and comprehensive overview of the two main SE techniques: searchable symmetric encryption (SSE) and public key encryption with keyword search (PEKS). Since the pioneering work of Song, Wagner, and Perrig (IEEE S&P '00), the field of provably secure SE has expanded to the point where we felt that taking stock would provide benefit to the community. The survey has been written primarily for the nonspecialist who has a basic information security background. Thus, we sacrifice full details and proofs of individual constructions in favor of an overview of the underlying key techniques. We categorize and compare the different SE schemes in terms of their security, efficiency, and functionality. For the experienced researcher, we point out connections between the many approaches to SE and identify open research problems. Two major conclusions can be drawn from our work. While the so-called IND-CKA2 security notion becomes prevalent in the literature and efficient (sublinear) SE schemes meeting this notion exist in the symmetric setting, achieving this strong form of security efficiently in the asymmetric setting remains an open problem. We observe that in multirecipient SE schemes, regardless of their efficiency drawbacks, there is a noticeable lack of query expressiveness that hinders deployment in practice.