Secure and Privacy Preserving Pattern Matching in Distributed Cloud-based Data Storage

Suppose a server holds a long text string and a receiver holds a short pattern string. Secure pattern matching allows the receiver to learn the locations in the long text where the pattern appears, while leaking nothing else to either party besides the length of their inputs. In this work we consider secure wildcard pattern matching (WPM), where the receiver’s pattern is allowed to contain wildcards that match to any character.

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.

Our main result in this paper is a parallel algorithm for suffix--prefix- (s--p-) matching that has optimal speedup on a concurrent-read/concurrent-write parallel random-access machine (CRCW PRAM). Given a string of length $m$, the algorithm runs in time $O(\log m)$ using $m/ \log m$ processors. This algorithm is important because we utilize s--p matching as a fundamental building block to solve several pattern- and string-matching problems, such as the following: {1. string matching; 2. multitext/multipattern string matching; 3. multidimensional pattern matching; 4. pattern-occurrence detection; 5. on-line string matching.} In particular, our techniques and algorithms are the first to preserve optimal speedup in the context of pattern matching in higher dimensions and are the only known ones to do so for dimensions $d > 2$.

Efficient protocols for private wildcards pattern matching

Parallel String Matching with Variable Length Don′t Cares

Cyber-attacks stealing confidential information are becoming increasingly frequent and devastating as modern software systems store and manipulate greater amounts of sensitive data. Leaking information about private user data, such as the financial and medical records of individuals, trade secrets of companies and military secrets of states can have drastic consequences. Confidentiality of such private data is critical for users of these systems. Many software development practices, such as the encryption of packages sent over a network, aim to protect the confidentiality of private data by ensuring that an observer is unable to learn anything meaningful about a program’s secret input from its public output. Under these protections, the software system’s main communication channels, such as the content of the network packets it sends, or the output it writes to a public file, should not leak information about the private data. However, many software systems still contain serious security vulnerabilities. Side channels are an important class of information leaks where secret information can be captured through the observation of non-functional side effects of software systems. Potential side channels include those in execution time, memory usage, size and timings of network packets, and power consumption. Although side-channel vulnerabilities due to hardware (such as vulnerabilities that exploit the cache behavior) have been extensively studied [1] , [2] , [10] , [13] , [15] – [17] , [19] , [23] , software side channels have only recently become an active area of research, including recent results on software side-channel detection [4] , [8] , [11] , [12] , [18] , [22] , [24] and quantification [5] , [20] , [21] , and my own work on a static analysis framework for detection of software side-channels called CoCo-Channel [8] ) and a constraint caching framework to accelerate side-channel quantification called Cashew [9] .

In the era of 5G, billions of terminal devices achieve global interconnection and intercommunication, which leads to the generation of massive data. However, the existing cloud‐based data‐sharing mechanism faces challenges such as sensitive information leakage and data islands, which makes it difficult to achieve secure sharing across domains. In this paper, the authors propose a fine‐grained data‐sharing scheme based on blockchain and ciphertext policy attribute‐based encryption, and design a verifiable outsourced computation method to reduce the computational pressure of end users. Second, the authors comprehensively consider the user's identity privacy and transaction privacy, and propose a multi‐level privacy protection method based on ring signature and garbled bloom filter, which enhance the user's data privacy and availability, and prevent the traceability of requests. Finally, the authors design a set of interconnected smart contracts, and verify that their scheme can achieve secure and efficient data sharing through security analysis and performance testing.

An Improved FAST+SURF Fast Matching Algorithm

Sequential recommender systems, as a specialized branch of recommender systems that can capture users’ dynamic preferences for more accurate and timely recommendations, have made significant progress. Recently, due to increasing concerns about user data privacy, some researchers have implemented federated learning for sequential recommendation, a.k.a., Federated Sequential Recommender Systems (FedSeqRecs), in which a public sequential recommender model is shared and frequently transmitted between a central server and clients to achieve collaborative learning. Although these solutions mitigate user privacy to some extent, they present two significant limitations that affect their practical usability: (1) They require a globally shared sequential recommendation model. However, in real-world scenarios, the recommendation model constitutes a critical intellectual property for platform and service providers. Therefore, service providers may be reluctant to disclose their meticulously developed models. (2) The communication costs are high as they correlate with the number of model parameters. This becomes particularly problematic as the current FedSeqRec will be inapplicable when sequential recommendation marches into a large language model era. To overcome the above challenges, this article proposes a parameter transmission-free federated sequential recommendation framework (PTF-FSR), which ensures both model and data privacy protection to meet the privacy needs of service providers and system users alike. Furthermore, since PTF-FSR only transmits prediction results under privacy protection, which are independent of model sizes, this new federated learning architecture can accommodate more complex and larger sequential recommendation models. Extensive experiments conducted on three widely used recommendation datasets, employing various sequential recommendation models from both ID-based and ID-free paradigms, demonstrate the effectiveness and generalization capability of our proposed framework. To facilitate future research in this direction, we release our code at https://github.com/hi-weiyuan/PTF-FSR .

For anomaly-based intrusion detection systems, software implementation of several string-matching algorithms using deep/machine learning approaches are frequently available. The research paper aims to explore the use of machine learning algorithms in developing intelligent intrusion detection systems for preventing unauthorized access to computer networks. A complete intrusion detection system consists of packet decoding, capturing, pre-processing, and pattern or string matching. However, string matching is computationally the most intensive part. Consequently, several architectures/designs have been proposed to accelerate the performance of string matching. This paper provides an architectural evaluation of the existing deep/machine learning-based solutions of string-matching algorithms. First, the most recent state-of-the-art solutions are carefully identified. Then, the identified solutions are compared in terms of the used dataset, number of classes, and accuracy. The architectural evaluation of multiple classification algorithms in this paper allows users of the domain to select an appropriate deep/machine learning algorithm for string matching according to their needs.

Exact single pattern string matching is a fundamental problem in computer science. To date, the performance of existing string matching algorithms for very short patterns is poor. In this article, based on the most basic exact single pattern string matching algorithm-BF, we presented a serial improved algorithms named HBF by introducing the q-grams method, the loop unrolling method, and modifying the smallest processing unit from byte to integer. Experimental results indicated that HBF is obviously faster than known algorithms for very short patterns on our platform. Meanwhile, the pre-processing phase of HBF just need constant time and space, the worst and the best time complexity are linear and O(n/w), separately, which w is the number of characters involved in an integer.

All algorithms below are optimal alphabet-independent parallel CRCW PRAM algorithms. In one dimension: Given a pattern string of length m for the string-matching problem, we design an algorithm that computes a deterministic sample of a sufficiently long substring in constant time. This problem used to be a bottleneck in the pattern preprocessing for one- and two-dimensional pattern matching. The best previous time bound was O(log/sup 2/ m/log log m). We use this algorithm to obtain the following results. 1. Improving the preprocessing of the constant-time text search algorithm from O(log/sup 2/ m/log log m) to n(log log m), which is now best possible. 2. A constant-time deterministic string-matching algorithm in the case that the text length n satisfies n=/spl Omega/(m/sup 1+/spl epsiv//) for a constant /spl epsiv/>0. 3. A simple probabilistic string-matching algorithm that has constant time with high probability for random input. 4. A constant expected time Las-Vegas algorithm for computing the period of the pattern and all witnesses and thus string matching itself, solving the main open problem remaining in string matching. >

Average complexity of backward q-gram string matching algorithms

We survey the current techniques to cope with the problem of string matching that allows errors. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on online searching and mostly on edit distance, explaining the problem and its relevance, its statistical behavior, its history and current developments, and the central ideas of the algorithms and their complexities. We present a number of experiments to compare the performance of the different algorithms and show which are the best choices. We conclude with some directions for future work and open problems.

In the parameterized string matching, a given pattern P is said to match with a substring t of the text T, if there exist a bijection from the symbols of P to the symbols of t. This problem has an important application in software maintenance, where we wish to find the equivalency between two sections of codes. Two sections of codes are said to be equivalent, if one can be transformed into the other by renaming identifiers and variables only. Crochemore et al., 1994, has developed an algorithm (BDM) for exact string matching problem using suffix automata. Kimmo Fredriksson et al., 2006, has developed parameterized bit-parallel algorithm (parameterized shift-or) and parameterized BDM (PBDM). Parameterized shift-or (PSO) simulates finite automata in their nondeterministic form. The main drawback of PSO is: it is unable to skip text characters while matching forward. In this paper, we develop a new algorithm for parameterized string matching problem. This algorithm is based upon both suffix automata and bit parallelism concepts. This algorithm is faster than PBDM, since it processes the suffix automata in their non-deterministic form.