Cloud computing has made it possible for a user to be able to select a computing service precisely when needed. However, certain factors such as security of data and regulatory issues will impact a user's choice of using such a service. A solution to these problems is the use of a hybrid cloud that combines a user's local computing capabilities (for mission- or organization-critical tasks) with a public cloud (for less influential tasks). We foresee three challenges that must be overcome before the adoption of a hybrid cloud approach: 1) data design: How to partition relations in a hybrid cloud? The solution to this problem must account for the sensitivity of attributes in a relation as well as the workload of a user; 2) data security: How to protect a user's data in a public cloud with encryption while enabling query processing over this encrypted data? and 3) query processing: How to execute queries efficiently over both, encrypted and unencrypted data? This paper addresses these challenges and incorporates their solutions into an add-on tool for a Hadoop and Hive based cloud computing infrastructure.
The area of privacy-preserving query processing has also received much attention @cite_11 @cite_14 . A homomorphic encryption based technique can be used to query over encrypted data @cite_17 but is expensive when the data size increases. We use techniques given in @cite_19 to preserve security of data. However, the difference between our work and @cite_19 is that we can store and query data locally unlike @cite_19 .
@cite_19 @cite_14 @cite_17 @cite_11
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Processing queries on encrypted data protects sensitive data stored in cloud databases. CryptDB has introduced the approach of adjustable encryption for such processing. A database column is adjusted to the necessary level of encryption, e.g. order-preserving, for the set of executed queries, but never reversed. This has the drawback that long running cloud databases will eventually transform into only order-preserving encrypted databases. In this paper we propose searchable encryption as an alternative in order to reduce this encryption degradation. It maintains security while only marginally impacting performance when applied only to infrequently used queries for searching. We present a budget-based encryption selection algorithm as part of query planning for making the appropriate choice between searchable and deterministic or order-preserving encryption. We evaluate our algorithm on a long-tail distributed TPC-C benchmark on an experimental implementation of encrypted queries in an in-memory database. In one choice of parameters our algorithm incurs only a (1.5 ) performance penalty, but one of 15 columns is not decrypted to order-preserving or deterministic encryption. Our selection algorithm is configurable, such that higher security gains are possible at the cost of performance. Abstract Increasingly, confidential medical records are being stored in data centers hosted by hospitals or large companies. As sophisticated algorithms for predictive analysis on medical data continue to be developed, it is likely that, in the future, more and more computation will be done on private patient data. While encryption provides a tool for assuring the privacy of medical information, it limits the functionality for operating on such data. Conventional encryption methods used today provide only very restricted possibilities or none at all to operate on encrypted data without decrypting it first. Homomorphic encryption provides a tool for handling such computations on encrypted data, without decrypting the data, and without even needing the decryption key. In this paper, we discuss possible application scenarios for homomorphic encryption in order to ensure privacy of sensitive medical data. We describe how to privately conduct predictive analysis tasks on encrypted data using homomorphic encryption. As a proof of concept, we present a working implementation of a prediction service running in the cloud (hosted on Microsoft’s Windows Azure), which takes as input private encrypted health data, and returns the probability for suffering cardiovascular disease in encrypted form. Since the cloud service uses homomorphic encryption, it makes this prediction while handling only encrypted data, learning nothing about the submitted confidential medical data. We propose a privacy-preserving framework for learning visual classifiers by leveraging distributed private image data. This framework is designed to aggregate multiple classifiers updated locally using private data and to ensure that no private information about the data is exposed during and after its learning procedure. We utilize a homomorphic cryptosystem that can aggregate the local classifiers while they are encrypted and thus kept secret. To overcome the high computational cost of homomorphic encryption of high-dimensional classifiers, we (1) impose sparsity constraints on local classifier updates and (2) propose a novel efficient encryption scheme named doubly-permuted homomorphic encryption (DPHE) which is tailored to sparse high-dimensional data. DPHE (i) decomposes sparse data into its constituent non-zero values and their corresponding support indices, (ii) applies homomorphic encryption only to the non-zero values, and (iii) employs double permutations on the support indices to make them secret. Our experimental evaluation on several public datasets shows that the proposed approach achieves comparable performance against state-of-the-art visual recognition methods while preserving privacy and significantly outperforms other privacy-preserving methods. Data security in the cloud is a big concern that blocks the widespread use of the cloud for relational data management. First, to ensure data security, data confidentiality needs to be provided when data resides in storage as well as when data is dynamically accessed by queries. Prior works on query processing on encrypted data did not provide data confidentiality guarantees in both aspects. Tradeoff between secrecy and efficiency needs to be made when satisfying both aspects of data confidentiality while being suitable for practical use. Second, to support common relational data management functions, various types of queries such as exact queries, range queries, data updates, insertion and deletion should be supported. To address these issues, this paper proposes a comprehensive framework for secure and efficient query processing of relational data in the cloud. Our framework ensures data confidentiality using a salted IDA encoding scheme and column-access-via-proxy query processing primitives, and ensures query efficiency using matrix column accesses and a secure B+-tree index. In addition, our framework provides data availability and integrity. We establish the security of our proposal by a detailed security analysis and demonstrate the query efficiency of our proposal through an experimental evaluation.