Reducing the Leakage in Practical Order-Revealing Encryption

We study practical order-revealing encryption (ORE) with a well-defined leakage profile (the information revealed about the plaintexts from their ciphertexts), a direction recently initiated by Chenette, Lewi, Weis, and Wu (CLWW). ORE, which allows public comparison of plaintext order via their ciphertexts, is a useful tool in the design of secure outsourced database systems. We first show a general construction of ORE with reduced leakage as compared to CLWW, by combining ideas from their scheme with a new type of \u27\u27property-preserving\u27\u27 hash function. We then show how to construct such a hash function efficiently based on bilinear maps. Our resulting ORE scheme is fairly practical: for n-bit plaintexts, ciphertexts consists of about 4n group elements, and order comparison requires about n^2 pairings. The leakage is, roughly speaking, the \u27\u27equality pattern\u27\u27 of the most-significant differing bits, whereas CLWW\u27s is the location and values of the most-significant differing bits. We also provide a generalization of our scheme that improves the leakage and/or efficiency. To analyze the quality of our leakage profile, we show several additional results. In particular, we show that order-\emph{preserving} (OPE) encryption, an important special case of ORE scheme in which ciphertexts are ordered, cannot be secure wrt.our leakage profile. This implies that our ORE scheme is the first one without multilinear maps that is proven secure wrt.a leakage profile unachievable by OPE. We also also show that our generalized scheme meets a \u27\u27semantically meaningful\u27\u27 one-wayness notion that schemes with the leakage of CLWW do not

Homomorphic Data Isolation for Hardware Trojan Protection

The interest in homomorphic encryption/decryption is increasing due to its excellent security properties and operating facilities. It allows operating on data without revealing its content. In this work, we suggest using homomorphism for Hardware Trojan protection. We implement two partial homomorphic designs based on ElGamal encryption/decryption scheme. The first design is a multiplicative homomorphic, whereas the second one is an additive homomorphic. We implement the proposed designs on a low-cost Xilinx Spartan-6 FPGA. Area utilization, delay, and power consumption are reported for both designs. Furthermore, we introduce a dual-circuit design that combines the two earlier designs using resource sharing in order to have minimum area cost. Experimental results show that our dual-circuit design saves 35% of the logic resources compared to a regular design without resource sharing. The saving in power consumption is 20%, whereas the number of cycles needed remains almost the sam

Exploring Privacy Preservation in Outsourced K-Nearest Neighbors with Multiple Data Owners

The k-nearest neighbors (k-NN) algorithm is a popular and effective classification algorithm. Due to its large storage and computational requirements, it is suitable for cloud outsourcing. However, k-NN is often run on sensitive data such as medical records, user images, or personal information. It is important to protect the privacy of data in an outsourced k-NN system. Prior works have all assumed the data owners (who submit data to the outsourced k-NN system) are a single trusted party. However, we observe that in many practical scenarios, there may be multiple mutually distrusting data owners. In this work, we present the first framing and exploration of privacy preservation in an outsourced k-NN system with multiple data owners. We consider the various threat models introduced by this modification. We discover that under a particularly practical threat model that covers numerous scenarios, there exists a set of adaptive attacks that breach the data privacy of any exact k-NN system. The vulnerability is a result of the mathematical properties of k-NN and its output. Thus, we propose a privacy-preserving alternative system supporting kernel density estimation using a Gaussian kernel, a classification algorithm from the same family as k-NN. In many applications, this similar algorithm serves as a good substitute for k-NN. We additionally investigate solutions for other threat models, often through extensions on prior single data owner systems

Hardware architecture implemented on FPGA for protecting cryptographic keys against side-channel attacks

This paper presents a new hardware architecture designed for protecting the key of cryptographic algorithms against attacks by side-channel analysis (SCA). Unlike previous approaches already published, the fortress of the proposed architecture is based on revealing a false key. Such a false key is obtained when the leakage information, related to either the power consumption or the electromagnetic radiation (EM) emitted by the hardware device, is analysed by means of a classical statistical method. In fact, the trace of power consumption (or the EM) does not reveal any significant sign of protection in its behaviour or shape. Experimental results were obtained by using a Virtex 5 FPGA, on which a 128-bit version of the standard AES encryption algorithm was implemented. The architecture could easily be extrapolated to an ASIC device based on standard cell libraries. The system is capable of concealing the real key when various attacks are performed on the AES algorithm, using two statistical methods which are based on correlation, the Welch’s t-test and the difference of means.Peer ReviewedPostprint (author's final draft

SoK: Cryptographically Protected Database Search

Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches. Since protected search was introduced in 2000, the area has grown rapidly; systems are offered by academia, start-ups, and established companies. However, there is no best protected search system or set of techniques. Design of such systems is a balancing act between security, functionality, performance, and usability. This challenge is made more difficult by ongoing database specialization, as some users will want the functionality of SQL, NoSQL, or NewSQL databases. This database evolution will continue, and the protected search community should be able to quickly provide functionality consistent with newly invented databases. At the same time, the community must accurately and clearly characterize the tradeoffs between different approaches. To address these challenges, we provide the following contributions: 1) An identification of the important primitive operations across database paradigms. We find there are a small number of base operations that can be used and combined to support a large number of database paradigms. 2) An evaluation of the current state of protected search systems in implementing these base operations. This evaluation describes the main approaches and tradeoffs for each base operation. Furthermore, it puts protected search in the context of unprotected search, identifying key gaps in functionality. 3) An analysis of attacks against protected search for different base queries. 4) A roadmap and tools for transforming a protected search system into a protected database, including an open-source performance evaluation platform and initial user opinions of protected search.Comment: 20 pages, to appear to IEEE Security and Privac

POPE: Partial Order Preserving Encoding

Recently there has been much interest in performing search queries over encrypted data to enable functionality while protecting sensitive data. One particularly efficient mechanism for executing such queries is order-preserving encryption/encoding (OPE) which results in ciphertexts that preserve the relative order of the underlying plaintexts thus allowing range and comparison queries to be performed directly on ciphertexts. In this paper, we propose an alternative approach to range queries over encrypted data that is optimized to support insert-heavy workloads as are common in "big data" applications while still maintaining search functionality and achieving stronger security. Specifically, we propose a new primitive called partial order preserving encoding (POPE) that achieves ideal OPE security with frequency hiding and also leaves a sizable fraction of the data pairwise incomparable. Using only O(1) persistent and $O(n^\epsilon)$ non-persistent client storage for $0<\epsilon<1$, our POPE scheme provides extremely fast batch insertion consisting of a single round, and efficient search with O(1) amortized cost for up to $O(n^{1-\epsilon})$ search queries. This improved security and performance makes our scheme better suited for today's insert-heavy databases.Comment: Appears in ACM CCS 2016 Proceeding

Conditionals in Homomorphic Encryption and Machine Learning Applications

Homomorphic encryption aims at allowing computations on encrypted data without decryption other than that of the final result. This could provide an elegant solution to the issue of privacy preservation in data-based applications, such as those using machine learning, but several open issues hamper this plan. In this work we assess the possibility for homomorphic encryption to fully implement its program without relying on other techniques, such as multiparty computation (SMPC), which may be impossible in many use cases (for instance due to the high level of communication required). We proceed in two steps: i) on the basis of the structured program theorem (Bohm-Jacopini theorem) we identify the relevant minimal set of operations homomorphic encryption must be able to perform to implement any algorithm; and ii) we analyse the possibility to solve -- and propose an implementation for -- the most fundamentally relevant issue as it emerges from our analysis, that is, the implementation of conditionals (requiring comparison and selection/jump operations). We show how this issue clashes with the fundamental requirements of homomorphic encryption and could represent a drawback for its use as a complete solution for privacy preservation in data-based applications, in particular machine learning ones. Our approach for comparisons is novel and entirely embedded in homomorphic encryption, while previous studies relied on other techniques, such as SMPC, demanding high level of communication among parties, and decryption of intermediate results from data-owners. Our protocol is also provably safe (sharing the same safety as the homomorphic encryption schemes), differently from other techniques such as Order-Preserving/Revealing-Encryption (OPE/ORE).Comment: 14 pages, 1 figure, corrected typos, added introductory pedagogical section on polynomial approximatio