Similarity search is essential to many important applications and often involves searching at scale on high-dimensional data based on their similarity to a query. In biometric applications, recent vulnerability studies have shown that adversarial machine learning can compromise biometric recognition systems by exploiting the biometric similarity information. Existing methods for biometric privacy protection are in general based on pairwise matching of secured biometric templates and have inherent limitations in search efficiency and scalability. In this paper, we propose an inference-based framework for privacy-preserving similarity search in Hamming space. Our approach builds on an obfuscated distance measure that can conceal Hamming distance in a dynamic interval. Such a mechanism enables us to systematically design statistically reliable methods for retrieving most likely candidates without knowing the exact distance values. We further propose to apply Montgomery multiplication for generating search indexes that can withstand adversarial similarity analysis, and show that information leakage in randomized Montgomery domains can be made negligibly small. Our experiments on public biometric datasets demonstrate that the inference-based approach can achieve a search accuracy close to the best performance possible with secure computation methods, but the associated cost is reduced by orders of magnitude compared to cryptographic primitives.
Privacy-enhanced variants of LSH are recently proposed by combining LSH with cryptographic or information-theoretic protocols @cite_0 . The privacy protection framework proposed in @cite_9 generates a partial query instance by omitting certain bits in one or more sub-hash values to increase the ambiguity of query information for the server. The hash values of retrieved candidates are returned to the client for refinement. The framework is extended in @cite_8 where partial encryption is performed on the hash code of each item to prevent an untrustworthy server from precisely linking queries and database records. In particular, the server uses the unencrypted part of each item for approximate indexing and search while the client uses the encrypted part for re-ranking the candidates received from the server. To limit the number of candidates sent to the client, the server performs a preliminary ranking based on the computed from the unencrypted part. The trade-off between privacy and utility of a search at the server side is therefore controlled by the number of unencrypted bits. We call this approach LSH + partial distance'' for brevity and will use it as one baseline approach for performance comparison.
@cite_0 @cite_9 @cite_8
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We propose a privacy protection framework for large-scale content-based information retrieval. It offers two layers of protection. First, robust hash values are used as queries to prevent revealing original content or features. Second, the client can choose to omit certain bits in a hash value to further increase the ambiguity for the server. Due to the reduced information, it is computationally difficult for the server to know the client’s interest. The server has to return the hash values of all possible candidates to the client. The client performs a search within the candidate list to find the best match. Since only hash values are exchanged between the client and the server, the privacy of both parties is protected. We introduce the concept oftunable privacy, where the privacy protection level can be adjusted according to a policy. It is realized through hash-based piecewise inverted indexing. The idea is to divide a feature vector into pieces and index each piece with a subhash value. Each subhash value is associated with an inverted index list. The framework has been extensively tested using a large image database. We have evaluated both retrieval performance and privacy-preserving performance for a particular content identification application. Two different constructions of robust hash algorithms are used. One is based on random projections; the other is based on the discrete wavelet transform. Both algorithms exhibit satisfactory performance in comparison with state-of-the-art retrieval schemes. The results show that the privacy enhancement slightly improves the retrieval performance. We consider the majority voting attack for estimating the query category and identification. Experiment results show that this attack is a threat when there are near-duplicates, but the success rate decreases with the number of omitted bits and the number of distinct items. This work proposes a privacy-protection framework for an important application called outsourced media search . This scenario involves a data owner, a client, and an untrusted server, where the owner outsources a search service to the server. Due to lack of trust, the privacy of the client and the owner should be protected. The framework relies on multimedia hashing and symmetric encryption. It requires involved parties to participate in a privacy-enhancing protocol. Additional processing steps are carried out by the owner and the client: (i) before outsourcing low-level media features to the server, the owner has to one-way hash them, and partially encrypt each hash-value; (ii) the client completes the similarity search by re-ranking the most similar candidates received from the server. One-way hashing and encryption add ambiguity to data and make it difficult for the server to infer contents from database items and queries, so the privacy of both the owner and the client is enforced. The proposed framework realizes trade-offs among strength of privacy enforcement, quality of search, and complexity, because the information loss can be tuned during hashing and encryption. Extensive experiments demonstrate the effectiveness and the flexibility of the framework. Abstract With the proliferation of smart grids, traditional utilities are struggling to handle the increasing amount of metering data. Outsourcing the metering data to heterogeneous distributed systems has the potential to provide efficient data access and processing. In an untrusted heterogeneous distributed system environment, employing data encryption prior to outsourcing can be an effective way to preserve user privacy. However, how to efficiently query encrypted multidimensional metering data stored in an untrusted heterogeneous distributed system environment remains a research challenge. In this paper, we propose a high performance and privacy-preserving query (P2Q) scheme over encrypted multidimensional big metering data to address this challenge. In the proposed scheme, encrypted metering data are stored in the server of an untrusted heterogeneous distributed system environment. A Locality Sensitive Hashing (LSH) based similarity search approach is then used to realize the similarity query. To demonstrate utility of the proposed LSH-based search approach, we implement a prototype using MapReduce for the Hadoop distributed environment. More specifically, for a given query, the proxy server will return K top similar data object identifiers. An enhanced Ciphertext-Policy Attribute-based Encryption (CP-ABE) policy is then used to control access to the search results. Therefore, only the requester with an authorized query attribute can obtain the correct secret keys to retrieve the metering data. We then prove that the P2Q scheme achieves data confidentiality and preserves the data owner’s privacy in a semi-trusted cloud. In addition, our evaluations demonstrate that the P2Q scheme can significantly reduce response time and provide high search efficiency without compromising on search quality (i.e. suitable for multidimensional big data search in heterogeneous distributed system, such as cloud storage system). Locality Sensitive Hashing (LSH) has emerged as the method of choice for high dimensional similarity search, a classical problem of interest in numerous applications. LSH-based solutions require that each data point be inserted into a number A of hash tables, after which a query can be answered by performing B lookups. The original LSH solution of [IM98] showed for the first time that both A and B can be made sublinear in the number of data points. Unfortunately, the classical LSH solution does not provide any tradeoff between insert and query complexity, whereas for data (respectively, query) intensive applications one would like to minimize insert time by choosing a smaller @math (respectively, minimize query time by choosing a smaller B). A partial remedy for this is provided by Entropy LSH [Pan06], which allows to make either inserts or queries essentially constant time at the expense of a loss in the other parameter, but no algorithm that achieves a smooth tradeoff is known. In this paper, we present an algorithm for performing similarity search under the Euclidean metric that resolves the problem above. Our solution is inspired by Entropy LSH, but uses a very different analysis to achieve a smooth tradeoff between insert and query complexity. Our results improve upon or match, up to lower order terms in the exponent, best known data-oblivious algorithms for the Euclidean metric.