Secure k-Nearest Neighbor Query over Encrypted Data in Outsourced Environments

For the past decade, query processing on relational data has been studied extensively, and many theoretical and practical solutions to query processing have been proposed under various scenarios. With the recent popularity of cloud computing, users now have the opportunity to outsource their data as well as the data management tasks to the cloud. However, due to the rise of various privacy issues, sensitive data (e.g., medical records) need to be encrypted before outsourcing to the cloud. In addition, query processing tasks should be handled by the cloud; otherwise, there would be no point to outsource the data at the first place. To process queries over encrypted data without the cloud ever decrypting the data is a very challenging task. In this paper, we focus on solving the k-nearest neighbor (kNN) query problem over encrypted database outsourced to a cloud: a user issues an encrypted query record to the cloud, and the cloud returns the k closest records to the user. We first present a basic scheme and demonstrate that such a naive solution is not secure. To provide better security, we propose a secure kNN protocol that protects the confidentiality of the data, user's input query, and data access patterns. Also, we empirically analyze the efficiency of our protocols through various experiments. These results indicate that our secure protocol is very efficient on the user end, and this lightweight scheme allows a user to use any mobile device to perform the kNN query.Comment: 23 pages, 8 figures, and 4 table

k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data

Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed k-NN protocol protects the confidentiality of the data, user's input query, and data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our solution through various experiments.Comment: 29 pages, 2 figures, 3 tables arXiv admin note: substantial text overlap with arXiv:1307.482

IO-DSSE: Scaling Dynamic Searchable Encryption to Millions of Indexes By Improving Locality

Free cloud-based services are powerful candidates for deploying ubiquitous encryption for messaging. In the case of email and increasingly chat, users expect the ability to store and search their messages persistently. Using data from one of the top three mail providers, we confirm that for a searchable encryption scheme to scale to millions of users, it should be highly IO-efficient (locality), and handle a very dynamic message corpi. We observe that existing solutions fail to achieve both properties simultaneously. We then design, build, and evaluate a provably secure Dynamic Searchable Symmetric Encryption (DSSE) scheme with significant reduction in IO cost compared to preceding works when used for email or other highly dynamic message corpi

STATIC ENFORCEMENT OF TERMINATION-SENSITIVE NONINTERFERENCE USING THE C++ TEMPLATE TYPE SYSTEM

A side channel is an observable attribute of program execution other than explicit communication, e.g., power usage, execution time, or page fault patterns. A side-channel attack occurs when a malicious adversary observes program secrets through a side channel. This dissertation introduces Covert C++, a library which uses template metaprogramming to superimpose a security-type system on top of C++’s existing type system. Covert C++ enforces an information-flow policy that prevents secret data from influencing program control flow and memory access patterns, thus obviating side-channel leaks. Formally, Covert C++ can facilitate an extended definition of the classical noninterference property, broadened to also cover the dynamic execution property of memory-trace obliviousness. This solution does not require any modifications to the compiler, linker, or C++ standard. To verify that these security properties can be preserved by the compiler (i.e., by compiler optimizations), this dissertation introduces the Noninterference Verification Tool (NVT). The NVT employs a novel dynamic analysis technique which combines input fuzzing with dynamic memory tracing. Specifically, the NVT detects when secret data influences a program’s memory trace, i.e., the sequence of instruction fetches and data accesses. Moreover, the NVT signals when a program leaks secret data to a publicly-observable storage channel. The Covert C++ library and the NVT are two components of the broader Covert C++ toolchain. The toolchain also provides a collection of refactoring tools to interactively transform legacy C or C++ code into Covert C++ code. Finally, the dissertation introduces libOblivious, a library to facilitate high-performance memory-trace oblivious computation with Covert C++

ZeroTrace : Oblivious Memory Primitives from Intel SGX

We are witnessing a confluence between applied cryptography and secure hardware systems in enabling secure cloud computing. On one hand, work in applied cryptography has enabled efficient, oblivious data-structures and memory primitives. On the other, secure hardware and the emergence of Intel SGX has enabled a low-overhead and mass market mechanism for isolated execution. By themselves these technologies have their disadvantages. Oblivious memory primitives carry high performance overheads, especially when run non-interactively. Intel SGX, while more efficient, suffers from numerous software-based side-channel attacks, high context switching costs, and bounded memory size. In this work we build a new library of oblivious memory primitives, which we call ZeroTrace. ZeroTrace is designed to carefully combine state-of-the-art oblivious RAM techniques and SGX, while mitigating individual disadvantages of these technologies. To the best of our knowledge, ZeroTrace represents the first oblivious memory primitives running on a real secure hardware platform. ZeroTrace simultaneously enables a dramatic speed-up over pure cryptography and protection from software-based side-channel attacks. The core of our design is an efficient and flexible block-level memory controller that provides oblivious execution against any active software adversary, and across asynchronous SGX enclave terminations. Performance-wise, the memory controller can service requests for 4~B blocks in 1.2~ms and 1~KB blocks in 3.4~ms (given a 10~GB dataset). On top of our memory controller, we evaluate Set/Dictionary/List interfaces which can all perform basic operations (e.g., get/put/insert)

Privacy-preserving query processing over encrypted data in cloud

The query processing of relational data has been studied extensively throughout the past decade. A number of theoretical and practical solutions to query processing have been proposed under various scenarios. With the recent popularity of cloud computing, data owners now have the opportunity to outsource not only their data but also data processing functionalities to the cloud. Because of data security and personal privacy concerns, sensitive data (e.g., medical records) should be encrypted before being outsourced to a cloud, and the cloud should perform query processing tasks on the encrypted data only. These tasks are termed as Privacy-Preserving Query Processing (PPQP) over encrypted data. Based on the concept of Secure Multiparty Computation (SMC), SMC-based distributed protocols were developed to allow the cloud to perform queries directly over encrypted data. These protocols protect the confidentiality of the stored data, user queries, and data access patterns from cloud service providers and other unauthorized users. Several queries were considered in an attempt to create a well-defined scope. These queries included the k-Nearest Neighbor (kNN) query, advanced analytical query, and correlated range query. The proposed protocols utilize an additive homomorphic cryptosystem and/or a garbled circuit technique at different stages of query processing to achieve the best performance. In addition, by adopting a multi-cloud computing paradigm, all computations can be done on the encrypted data without using very expensive fully homomorphic encryptions. The proposed protocols\u27 security was analyzed theoretically, and its practicality was evaluated through extensive empirical results --Abstract, page iii

Applying Secure Multi-party Computation in Practice

In this work, we present solutions for technical difficulties in deploying secure multi-party computation in real-world applications. We will first give a brief overview of the current state of the art, bring out several shortcomings and address them. The main contribution of this work is an end-to-end process description of deploying secure multi-party computation for the first large-scale registry-based statistical study on linked databases. Involving large stakeholders like government institutions introduces also some non-technical requirements like signing contracts and negotiating with the Data Protection Agency

Repeatable Oblivious Shuffling of Large Outsourced Data Blocks

As data outsourcing becomes popular, oblivious algorithms have raised extensive attentions since their control flow and data access pattern appear to be independent of the input data they compute on and thus are especially suitable for secure processing in outsourced environments. In this work, we focus on oblivious shuffling algorithms that aim to shuffle encrypted data blocks outsourced to the cloud server without disclosing the permutation of blocks to the server. Existing oblivious shuffling algorithms suffer from issues of heavy communication and client computation costs when blocks have a large size because all outsourced blocks must be downloaded to the client for shuffling or peeling off extra encryption layers. To help eliminate this void, we introduce the ``repeatable oblivious shuffling\u27\u27 notation that restricts the communication and client computation costs to be independent of the block size. We present an efficient construction of repeatable oblivious shuffling using additively homomorphic encryption schemes. The comprehensive evaluation of our construction shows its effective usability in practice for shuffling large-sized blocks