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.
We have also identified a recent work, called Relational Cloud @cite_11 , that attempts to address the problems we have identified above. The difference between our work and Relational Cloud is that our data partitioning scheme considers the cost of querying encrypted attributes stored on a public cloud. Relational Cloud partitions data using a graph-based partitioning scheme without attaching any query cost constraints. These partitions are then encrypted with multiple layers of encryption and stored on a server. A query is then executed over the encrypted data with multiple rounds of communication between a client and server without considering the cost of decrypting intermediate relations. In our work, we explicitly consider the cost of queries that involve all three components of a hybrid cloud: a query over data in a private cloud, a query over non-sensitive (i.e., unencrypted) data and, a query over sensitive (i.e., encrypted) data on a public cloud. To the best of our knowledge, ours is the first work to explicitly estimate the cost of querying over unencrypted and encrypted data in a distributed setting.
@cite_11
2108590723 1544010666 2651887877 2105746252
As Cloud Computing becomes prevalent, sensitive information are being increasingly centralized into the cloud. For the protection of data privacy, sensitive data has to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow users to securely search over encrypted data through keywords, these techniques support only boolean search, without capturing any relevance of data files. This approach suffers from two main drawbacks when directly applied in the context of Cloud Computing. On the one hand, users, who do not necessarily have pre-knowledge of the encrypted cloud data, have to post process every retrieved file in order to find ones most matching their interest, On the other hand, invariably retrieving all files containing the queried keyword further incurs unnecessary network traffic, which is absolutely undesirable in today's pay-as-you-use cloud paradigm. In this paper, for the first time we define and solve the problem of effective yet secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by returning the matching files in a ranked order regarding to certain relevance criteria (e.g., keyword frequency), thus making one step closer towards practical deployment of privacy-preserving data hosting services in Cloud Computing. We first give a straightforward yet ideal construction of ranked keyword search under the state-of-the-art searchable symmetric encryption (SSE) security definition, and demonstrate its inefficiency. To achieve more practical performance, we then propose a definition for ranked searchable symmetric encryption, and give an efficient design by properly utilizing the existing cryptographic primitive, order-preserving symmetric encryption (OPSE). Thorough analysis shows that our proposed solution enjoys as-strong-as-possible" security guarantee compared to previous SSE schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution. This paper introduces a new transactional “database-as-a-service” (DBaaS) called Relational Cloud. A DBaaS promises to move much of the operational burden of provisioning, configuration, scaling, performance tuning, backup, privacy, and access control from the database users to the service operator, offering lower overall costs to users. Early DBaaS efforts include Amazon RDS and Microsoft SQL Azure, which are promising in terms of establishing the market need for such a service, but which do not address three important challenges: efficient multi-tenancy, elastic scalability, and database privacy. We argue that these three challenges must be overcome before outsourcing database software and management becomes attractive to many users, and cost-effective for service providers. The key technical features of Relational Cloud include: (1) a workload-aware approach to multi-tenancy that identifies the workloads that can be co-located on a database server, achieving higher consolidation and better performance than existing approaches; (2) the use of a graph-based data partitioning algorithm to achieve near-linear elastic scale-out even for complex transactional workloads; and (3) an adjustable security scheme that enables SQL queries to run over encrypted data, including ordering operations, aggregates, and joins. An underlying theme in the design of the components of Relational Cloud is the notion of workload awareness: by monitoring query patterns and data accesses, the system obtains information useful for various optimization and security functions, reducing the configuration effort for users and operators. 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. The emergence of cost-effective cloud services offers organizations great opportunity to reduce their cost and increase productivity. This development, however, is hampered by privacy concerns: a significant amount of organizational computing workload at least partially involves sensitive data and therefore cannot be directly outsourced to the public cloud. The scale of these computing tasks also renders existing secure outsourcing techniques less applicable. A natural solution is to split a task, keeping the computation on the private data within an organization's private cloud while moving the rest to the public commercial cloud. However, this hybrid cloud computing is not supported by today's data-intensive computing frameworks, MapReduce in particular, which forces the users to manually split their computing tasks. In this paper, we present a suite of new techniques that make such privacy-aware data-intensive computing possible. Our system, called Sedic, leverages the special features of MapReduce to automatically partition a computing job according to the security levels of the data it works on, and arrange the computation across a hybrid cloud. Specifically, we modified MapReduce's distributed file system to strategically replicate data, moving sanitized data blocks to the public cloud. Over this data placement, map tasks are carefully scheduled to outsource as much workload to the public cloud as possible, given sensitive data always stay on the private cloud. To minimize inter-cloud communication, our approach also automatically analyzes and transforms the reduction structure of a submitted job to aggregate the map outcomes within the public cloud before sending the result back to the private cloud for the final reduction. This also allows the users to interact with our system in the same way they work with MapReduce, and directly run their legacy code in our framework. We implemented Sedic on Hadoop and evaluated it using both real and synthesized computing jobs on a large-scale cloud test-bed. The study shows that our techniques effectively protect sensitive user data, offload a large amount of computation to the public cloud and also fully preserve the scalability of MapReduce.