The basic pattern matching problem is to find the locations where a pattern occurs in a text. Recently, secure pattern matching has been received much attention in various areas, including privacy-preserving DNA matching and secure biometric authentication. The aim of this paper is to give a practical solution for this problem using homomorphic encryption, which is public key encryption supporting some operations on encrypted data.In this paper, we make use of the somewhat homomorphic encryption scheme presented by Lauter, Naehrig and Vaikuntanathan (ACM CCSW 2011), which supports a limited number of both additions and multiplications on encrypted data. In their work, some message encoding techniques are also presented for enabling us to efficiently compute sums and products over the integers. Based on their techniques, we propose a new packing method suitable for an efficient computation of multiple Hamming distance values on encrypted data. Our main extension gives two types of packed ciphertexts, and a linear computation over packed ciphertexts gives our desired results. We implemented the scheme with our packing method.Our experiments ran in an Intel Xeon at 3.07 GHz with our software library using inline assembly language in C programs. Our optimized implementation shows that the packed encryption of a text or a pattern, the computation of multiple Hamming distance values over packed ciphertexts, and the decryption respectively take about 3.65 milliseconds (ms), 5.31 ms, and 3.47 ms for secure exact and approximate pattern matching of a binary text of length 2048. The total time is about 12.43 ms, which would give the practical performance in real life. Our method gives both faster performance and lower communication than the state-of-the-art work for a binary text of several thousand bits in length.

Secure pattern matching using somewhat homomorphic encryption

Today’s businesses increasingly rely on cloud computing, which brings both great opportunities and challenges. One of the critical challenges is resiliency: disruptions due to failures (either accidental or because of disasters or attacks) may entail significant revenue losses (e.g., US$ 25.5 billion in 2010 for North America). Such failures may originate at any of the major components in a cloud architecture (and propagate to others): 1) the servers hosting the application; 2) the network interconnecting them (on different scales, inside a data center, up to wide-area connections); or 3) the application itself. We comprehensively survey a large body of work focusing on resilience of cloud computing, in each (or a combination) of the server, network, and application components. First, we present the cloud computing architecture and its key concepts. We highlight both the infrastructure (servers, network) and application components. A key concept is virtualization of infrastructure (i.e., partitioning into logically separate units), and thus we detail the components in both physical and virtual layers. Before moving to the detailed resilience aspects, we provide a qualitative overview of the types of failures that may occur (from the perspective of the layered cloud architecture), and their consequences. The second major part of the paper introduces and categorizes a large number of techniques for cloud computing infrastructure resiliency. This ranges from designing and operating the facilities, servers, networks, to their integration and virtualization (e.g., also including resilience of the middleware infrastructure). The third part focuses on resilience in application design and development. We study how applications are designed, installed, and replicated to survive multiple physical failure scenarios as well as disaster failures.

/pdf/a-survey-on-resiliency-techniques-in-cloud-computing-2hn3780zwf.pdf

A Survey on Resiliency Techniques in Cloud Computing Infrastructures and Applications

In this paper, we consider a setting where a client wants to outsource storage of a large amount of private data and then perform substring search queries on the data – given a data string s and a search string p, find all occurrences of p as a substring of s. First, we formalize an encryption paradigm that we call queryable encryption, which generalizes searchable symmetric encryption (SSE) and structured encryption. Then, we construct a queryable encryption scheme for substring queries. Our construction uses suffix trees and achieves asymptotic efficiency comparable to that of unencrypted suffix trees. Encryption of a string of length n takes O(λn) time and produces a ciphertext of size O(λn), and querying for a substring of length m that occurs k times takes O(λm + k) time and three rounds of communication. Our security definition guarantees correctness of query results and privacy of data and queries against a malicious adversary. Following the line of work started by Curtmola et al. (ACM CCS 2006), in order to construct more efficient schemes we allow the query protocol to leak some limited information that is captured precisely in the definition. We prove security of our substring-searchable encryption scheme against malicious adversaries, where the query protocol leaks limited information about memory access patterns through the suffix tree of the encrypted string.

/pdf/substring-searchable-symmetric-encryption-3yf60xtqj5.pdf

Substring-Searchable Symmetric Encryption

Passive millimeter-wave (PMMW) imagers using a single radiometer, called single pixel imagers, employ raster scanning to produce images. A serious drawback of such a single pixel imaging system is the long acquisition time needed to produce a high-fidelity image, arising from two factors: (a) the time to scan the whole scene pixel by pixel and (b) the integration time for each pixel to achieve adequate signal to noise ratio. Recently, compressive sensing (CS) has been developed for single-pixel optical cameras to significantly reduce the imaging time and at the same time produce high-fidelity images by exploiting the sparsity of the data in some transform domain. While the efficacy of CS has been established for single-pixel optical systems, its application to PMMW imaging is not straightforward due to its (a) longer wavelength by three to four orders of magnitude that suffers high diffraction losses at finite size spatial waveform modulators and (b) weaker radiation intensity, for example, by eight orders of magnitude less than that of infrared. We present the development and implementation of a CS technique for PMMW imagers and shows a factor-of-ten increase in imaging speed.

https://www.spiedigitallibrary.org/journals/optical-engineering/volume-51/issue-9/091614/Passive-millimeter-wave-imaging-with-compressive-sensing/10.1117/1.OE.51.9.091614.pdf

Passive millimeter-wave imaging with compressive sensing

We propose a protocol for the problem of secure two-party pattern matching, where Alice holds a text t?{0,1}? of length n, while Bob has a pattern p?{0,1}? of length m. The goal is for Bob to (only) learn where his pattern occurs in Alice's text, while Alice learns nothing. Private pattern matching is an important problem that has many applications in the area of DNA search, computational biology and more. Our construction guarantees full simulation in the presence of malicious, polynomial-time adversaries (assuming the hardness of DDH assumption) and exhibits computation and communication costs of O(n+m) group elements in a constant round complexity. This improves over previous work by Gennaro et al. (Public Key Cryptography, pp. 145---160, 2010) whose solution requires overhead of O(nm) group elements and exponentiations in O(m) rounds. In addition to the above, we propose a collection of protocols for important variations of the secure pattern matching problem that are significantly more efficient than the current state of art solutions: First, we deal with secure pattern matching with wildcards. In this variant the pattern may contain wildcards that match both 0 and 1. Our protocol requires O(n+m) communication and O(1) rounds using O(nm) computation. Then we treat secure approximate pattern matching. In this variant the matches may be approximated, i.e., have Hamming distance less than some threshold, ?. Our protocol requires O(n?) communication in O(1) rounds using O(nm) computation. Third, we have secure pattern matching with hidden pattern length. Here, the length, m, of Bob's pattern remains a secret. Our protocol requires O(n+M) communication in O(1) rounds using O(n+M) computation, where M is an upper bound on m. Finally, we have secure pattern matching with hidden text length. Finally, in this variant the length, n, of Alice's text remains a secret. Our protocol requires O(N+m) communication in O(1) rounds using O(N+m) computation, where N is an upper bound on n.

/pdf/computationally-secure-pattern-matching-in-the-presence-of-4xpp9rng3d.pdf

Computationally Secure Pattern Matching in the Presence of Malicious Adversaries

In this paper we consider the problem of secure pattern matching that allows single character wildcards and substring matching in the malicious (stand-alone) setting. Our protocol, called 5PM, is executed between two parties: Server, holding a text of length n, and Client, holding a pattern of length m to be matched against the text, where our notion of matching is more general and includes non-binary alphabets, non-binary Hamming distance and non-binary substring matching.

5PM is the first protocol with communication complexity sub-linear in circuit size to compute non-binary substring matching in the malicious model (general MPC has communication complexity which is at least linear in the circuit size). 5PM is also the first sublinear protocol to compute non-binary Hamming distance in the malicious model. Additionally, in the honest-but-curious (semi-honest) model, 5PM is asymptotically more efficient than the best known scheme when amortized for applications that require single charcter wildcards or substring pattern matching. 5PM in the malicious model requires O((m+n)k2) bandwidth and O(m+n) encryptions, where m is the pattern length and n is the text length. Further, 5PM can hide pattern size with no asymptotic additional costs in either computation or bandwidth. Finally, 5PM requires only 2 rounds of communication in the honest-but-curious model and 8 rounds in the malicious model. Our techniques reduce pattern matching and generalized Hamming distance problem to a novel linear algebra formulation that allows for generic solutions based on any additively homomorphic encryption. We believe our efficient algebraic techniques are of independent interest.

/pdf/5pm-secure-pattern-matching-4pzqczgjpr.pdf

5PM: secure pattern matching

In this paper we consider the problem of secure pattern matching that allows single-character wildcards and substring matching in the malicious stand-alone setting. Our protocol, called 5PM, is executed between two parties: Server, holding a text of length n, and Client, holding a pattern of length m to be matched against the text, where our notion of matching is more general than traditionally considered and includes non-binary alphabets, non-binary Hamming distance and non-binary substring matching.5PM is the first secure expressive pattern matching protocol designed to optimize round complexity by carefully specifying the entire protocol round by round. 5PM requires only eight rounds in the malicious static corruptions model. In the malicious model, 5PM requires O((m+n)k2) communication complexity and O(m+n) encryptions, where m is the pattern length and n is the text length. Further, 5PM can hide pattern size with no asymptotic additional costs in either computation or bandwidth.

/pdf/5pm-secure-pattern-matching-2fmyng0m6k.pdf

5PM: Secure pattern matching

Described is a system for allowing sets of processors to engage in a secure pattern matching protocol. An input pattern is received from a first set of processors, while a text is received from a second set of processors. A matrix is constructed based on values computed for each character determined by each character's position in the pattern. The first set of processors sends an encrypted matrix to the second set of processors. The second set of processors processes each character in the text and creates a set of vectors. A final activation vector is created based on processing the set of vectors and an encrypted activation vector. The second set of processors sends the final activation vector to the first set of processors. The second set of processors decrypts the final activation vector. The system provides to the first set of processors where the pattern matches the text.

Secure pattern matching

Described is a system for proactively secure multi-party computation (MPC) Secret shares representing data are constructed to perform computations between a plurality of parties modeled as probabilistic polynomial-time interactive turing machines A number of rounds of communication where the plurality of parties jointly compute on the secret shares is specified Additionally, a threshold of a number of the plurality of parties that can be corrupted by an adversary is specified The secret shares are periodicially refreshed and reshared among the plurality of parties before and after computations in each of the rounds of communication The data the secret shares represent is proactively secured

General protocol for proactively secure computation

The authors present an analysis of compressive sensing (CS) as applied to millimeter wave and optical
imaging systems, showing that the technique inherently reduces detection efficiency due to reflection and diffraction
effects of the underlying electromagnetics. The results show that single-detector imaging approaches that rely on
simultaneous detection of multiple spatial modes (i.e., image pixels) require an electrically large detector to maintain
high detection efficiency.

Joshua W. Baron

Papers

5PM: secure pattern matching

5PM: Secure pattern matching

Secure pattern matching

General protocol for proactively secure computation

Performance Limitations of Compressive Sensing for Millimeter Wave Imaging