Encrypted Dataset Research Articles

MADESANT: malware detection and severity analysis in industrial environments

Malware remains a persistent threat to industrial operations, causing disruptions and financial losses. Traditional malware detection approaches struggle with the increasing complexity of false positives and negatives. However, existing Intrusion Detection Systems (IDSs) often lack the capability to assess the severity of detected malware, crucial for effective threat mitigation. This paper presents a novel model, MAlware DEtection and Severity Analysis for eNcrypted Traffic (MADESANT), designed to detect and analyze malware severity in encrypted traffic data. MADESANT combines Deep Learning (DL)-based intrusion detection with Machine Learning (ML)-based severity analysis, specifically customized for the minutiae of IoT systems and assets. Notably, MADESANT introduces a cascading model integrating a Cascading Forward Back Propagation Neural Network (CFBPNN) with the J48 tree to systematically assess risk factors in network traffic. Our assessment, conducted on diverse encrypted datasets including UNSW-NB15, IoT23, and XIIoTID, highlights the remarkable efficacy of MADESANT. Impressively, it achieves a flawless 0% false positive rate in detecting binary attack instances, surpassing benchmarks set by conventional models. Additionally, MADESANT excels in accurately estimate malware severity, providing invaluable insights into the factors contributing to the risk. To further validate its efficiency, we compared MADESANT against prevalent Neural Network models like FeedForward and Recurrent Neural Networks, with MADESANT emerging as the superior choice. The experimentation encompasses both the entire dataset and subsets generated through meticulous risk factor analysis. These results underscore MADESANT’s prowess in not only identifying malware but also in evaluating its potential impact, signifying a significant leap forward in industrial cybersecurity.

Attribute-Based Searchable Encryption: A Survey

With the advent of the big data era, the size and complexity of data continue to increase, which makes the requirement for data privacy and security increasingly urgent. However, traditional encryption methods cannot meet the demand for efficient searching in large-scale datasets. To solve this problem and enable users to search within encryped data and without decrypting the entire dataset, trapdoor functions and other cryptograhic techniques are introduced in searchable encryption. However, searchable encryption still cannot meet the needs in the real world. Therefore, researchers have introduced the concept of attribute-based encryption into searchable encryption, resulting in attribute-based searchable encryption (ABSE). This approach aims to achieve efficient search by attributes in encrypted datasets. ABSE has a wide range of applications in the fields of privacy protection, data sharing, and cloud computing. In this paper, we describe the trends in development, focusing on enhancing security, improving computational efficiency, and increasing flexibility. We also present the related schemes. In addition, several common application areas are introduced and the relevant schemes proposed by researchers are summarized. Moreover, the challenges and future directions of ABSE are discussed in this paper.

A cloud‐based solution for trustless indigenous data sovereignty: Protecting Māori biodiversity management data in Aotearoa New Zealand

AbstractIndigenous peoples should be able to govern data about themselves, their territories, resources, and ways of life, collected by themselves or others. However, the progressive use of cloud computing for the geoweb raises data security and privacy concerns. We propose a complete and trustless approach for indigenous geospatial data sovereignty on the cloud by furnishing security functions at the core—the web browser. Geomasking permits sharing an anonymized dataset with less privileged users, while the original is protected and shared with sovereign data owners via public‐key encryption. The encrypted dataset's hash value is notarized on the blockchain for the verification of its authenticity when on the cloud. The application was designed for the protection of Biodiversity Management Areas stewarded by the Māori people in Aotearoa New Zealand. It enables diversified functions of geospatial data protection compared with previous works focusing on the cloud by solving data‐sharing problems without relying on a third party.

An In-Depth Analysis on Efficiency and Vulnerabilities on a Cloud-Based Searchable Symmetric Encryption Solution

Searchable Symmetric Encryption (SSE) has come to be as an integral cryptographic approach in a world where digital privacy is essential. The capacity to search through encrypted data whilst maintaining its integrity meets the most important demand for security and confidentiality in a society that is increasingly dependent on cloud-based services and data storage. SSE offers efficient processing of queries over encrypted datasets, allowing entities to comply with data privacy rules while preserving database usability. Our research goes into this need, concentrating on the development and thorough testing of an SSE system based on Curtmola’s architecture and employing Advanced Encryption Standard (AES) in Cypher Block Chaining (CBC) mode. A primary goal of the research is to conduct a thorough evaluation of the security and performance of the system. In order to assess search performance, a variety of database settings were extensively tested, and the system's security was tested by simulating intricate threat scenarios such as count attacks and leakage abuse. The efficiency of operation and cryptographic robustness of the SSE system are critically examined by these reviews.

Uncovering Malware Families Using Convolutional Neural Networks (CNN)

Malware attacks pose significant cyber threats, with a rising number of vulnerability reports in security communities due to the continual introduction of mutations by malware programmers to evade detection. One of the most attractive targets which attacked by malware is the organization emails system. Malware’s mutations within the malware family, has complicating the development of effective machine learning-based malware analysis and classification methods. To answer this challenge, this research uses an agnostic deep learning solution inspired by ImageNet's success, which efficiently classifies malware into families by analyzing visual representations of malicious software as greyscale images using a Convolutional Neural Network (CNN). The Malwizard is a flexible Python tool suitable for both organizations and end-users enabling automated and rapid malware analysis within email system. Malwizard could be use as an Outlook Email’s add-in and an API service for SOAR platforms. The study evaluates this novel approach using the Microsoft Classification Challenge dataset, where image representations are encrypted to address privacy concerns. Experimental results show that the proposed approach performs comparably to the best existing model on plain text data, accomplishing the task in one-third of the time. For the encrypted dataset, adjustments to classical techniques are necessary for improved efficiency.

Fast and private multi-dimensional range search over encrypted data

For businesses looking to outsource their data to remote servers, cloud-based data storage is a popular choice. It is popular due to its flexibility, cost-effectiveness, and widespread availability. However, ensuring the confidentiality of data is a critical challenge that must be addressed. As a response to this issue, searchable encryption techniques have been developed. These techniques enable search queries to be performed on encrypted data while still keeping the plaintext confidential. While most existing symmetric searchable encryption schemes are designed for one-dimensional data records or document-keyword inverted indices, this paper introduces MDRSSE, a novel symmetric searchable encryption scheme specifically tailored for multi-dimensional range search. MDRSSE stands out as one of the pioneering SSE schemes to support multi-dimensional range search efficiently, without incurring undetermined additional communication or computation costs. By employing a single round of communication between the client and server, MDRSSE enables an honest-but-curious server to respond to multi-dimensional range queries without gaining knowledge of the data records or revealing the search query. Notably, MDRSSE boasts the lowest overall search complexity compared to existing state-of-the-art symmetric searchable encryption schemes designed for multi-dimensional range search. Extensive experimental tests were conducted to validate the robustness and practicality of our proposed scheme. The results demonstrate that, for a dataset consisting of 100K records with 12 dimensions (with each leaf node holding 500 records), it takes only 2.2 seconds to generate the encrypted dataset, and the overall setup phase completes within 2.5 seconds. Furthermore, for a range query encompassing 50 nodes, the search time is less than 2 ms and 3 ms for the client and server, respectively. MDRSSE achieves semantic security under the IND-CPA assumption, all without requiring additional storage size at the server.

An Attribute-Based Searchable Encryption Scheme for Cloud-Assisted IIoT

The searchable encryption is a particular case of structured encryption, which has been intensively researched in the secure cloud storage system. By constructing structured secure index such as encrypted multi-maps (EMM), encrypted inverted index (EII), etc., searchable encryption can achieve efficient keyword search over encrypted data set. However, existing searchable encryption constructions do not take search permissions into consideration, resulting in the lack of a mechanism of the data access control, which may not be suitable for IIoT applications, since an integrated industrial system contains all kinds of data with rigorous access permissions. In this paper, we construct an attribute-based searchable encryption construction for cloud-assisted IIoT application scenario. By designing the novel access policy based structured secure index and the attribute-based search token, our construction achieves fine-grained keyword search privilege control over encrypted IIoT data as well as the same search complexity as the traditional searchable encryption. To the best of our knowledge, this is the first Attribute-Based Searchable Encryption (ABSE) construction. We provide the correctness and security proofs for our construction. Experimental evaluation results in a real-world data set show the correctness and the practical search efficiency of the proposed ABSE.

Towards Efficient and Privacy-Preserving Interval Skyline Queries Over Time Series Data

Outsourcing encrypted time series data and query services to a cloud has been widely adopted by data owners for economic considerations. However, it inevitably lowers data utility and query efficiency. Existing secure skyline query schemes either leak critical information or are inefficient. In this paper, we propose an efficient and privacy-preserving interval skyline query scheme by employing symmetric homomorphic encryption (SHE). Specifically, we first devise a secure sort protocol to sort the encrypted dataset and a secure high-dimensional dominance check protocol to securely determine dominance relations of time series data, in which a dominance check tree is presented. With these secure protocols, we propose our secure skyline computation protocol that can ensure both security and efficiency. Furthermore, to deal with the characteristics of time series data, we design a look-up table to index time series for quick query response. The security analysis shows that our proposed scheme can protect outsourced data, query results, and single-dimensional privacy and hide access patterns. In addition, we evaluate our proposed scheme and compare the core component of our scheme with the state-of-the-art solution, and the results indicate that our protocol outperforms the compared solution by two orders of magnitude in the computational cost and at least 23× in the communication cost.

Blockchains as a means to promote privacy protecting, access availing, incentive increasing, ELSI lessening DNA databases.

Not all blockchains are created equal, and many cannot accommodate all of the primary characteristics of big data: Variety, Velocity, Volume and Veracity. Currently, public blockchains are slow and clunky, it can be expensive to keep up with the velocity of genomic data production. Further, the transparent and universally accessible nature of public blockchain doesn't necessarily accommodate all of the variety of sequence data, including very private information. Bespoke private permissioned blockchains, however, can be created to optimally accommodate all of the big data features of genomic data. Further, private permissioned chains can be implemented to both protect the privacy and security of the genetic information therein, while also providing access to researchers. An NFT marketplace associated with that private chain can provide the discretized sale of anonymous and encrypted data sets while also incentivizing individuals to share their data through payments mediated by smart contracts. Private blockchains can provide a transparent chain of custody for each use of the customers' data, and validation that this data is not corrupted. However, even with all of these benefits there remain some concerns with the implementation of this new technology including the ethical, legal and social implications typically associated with DNA databases.

A Dynamic-Efficient Structure for Secure and Verifiable Location-Based Skyline Queries

In a broad range of commercial and government applications, supporting secure location-based query services over outsourced cloud-based services particularly for data update on encrypted datasets remains challenging in practice. Compounding the challenge is the need to ensure update and query efficiency, dataset confidentiality (including against potentially malicious cloud service providers) and query authenticity. Thus in this paper, we propose DynPilot, a novel solution for privacy-preserving verifiable location-based skyline queries over dynamic and encrypted data(sets). The key challenge is how to devise a ciphertext-based authenticated data structure (ADS) that not only protects the confidentiality of the dataset (including the verification phase), but also the effective maintenance of such a dataset. Moreover, to motivate the cloud into actively updating ADS, the digest of the raw dataset is stored in the blockchain due to its immutability and consensus mechanism where update cost is also considered. Therefore, we present a novel ADS (hereafter referred to as <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> ynamic-Efficient <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> ecure and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</u> erifiable <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ree (DSV-tree)), designed to be dynamic and support secure and verifiable skyline queries. Meanwhile, DynPilot also achieves forward privacy using a novel fuzzy update strategy. To further improve the efficiency of queries, an optimized version (i.e., DSV*-tree) is also developed based on the idea of the multi-level index structure. Finally, we analyze the security and complexity of our approach, and the empirical evaluations demonstrate the utility of our approach.

Multi-Key Similar Data Search on Encrypted Storage With Secure Pay-Per-Query

Many commercial cloud service providers (CSPs) adopt pay-per-query pricing models, in which data owners are charged based on the amount of data scanned by each query. In such a data sharing model, not only the privacy preservation for the data and queries but also the trustworthiness of the underlying billing system is of the utmost importance. In this paper, we revisit multi-key searchable encryption (MKSE), an efficient and secure data search algorithm allowing a data owner to grant users the ability to retrieve data of interest over the outsourced, encrypted datasets. We first investigate which factor in existing MKSE schemes renders authorized users over-privileged such that, without risking their credits (e.g., leaking the private keys and/or the passwords for their accounts associated with a project where the shared data resides), they can allow unauthorized users to make valid queries. Unfortunately, this concern may be devastating because the queries made by unauthorized users would incur unexpected financial damage to the owner in practical pay-per-query models. We then propose a novel multi-key data search scheme that is resilient to unauthorized queries. The proposed scheme features a novel user authorization mechanism that carefully limits user privilege such that even an authorized user cannot illegally invite unauthorized users to query unless he entirely leaks his credit. We demonstrate the proposed scheme is comparable to prior work in terms of performance while achieving a higher level of security.

Homomorphically counting elements with the same property

We propose homomorphic algorithms for privacy-preserving applications where we are given an encrypted dataset and we want to compute the number of elements that share a common property. We consider a two party scenario between a client and a server, where the storage and computation is outsourced to the server. We present two new efficient methods to solve this problem by homomorphically evaluating a selection function encoding the desired property, and counting the number of elements which evaluates to the same value. Our first method programs the homomorphic computation in the style of the the functional bootstrapping of TFHE and can be instantiated with essentially any homomorphic encryption scheme that operates on polynomials, like FV or BGV. Our second method relies on new homomorphic operations and ciphertext formats, and it is more suitable for applications where the number of possible inputs is much larger than the number of possible values for the property. We illustrate the feasibility of our methods by presenting a publicly available proof-ofconcept implementation in C++ and using it to evaluate a heatmap function over encrypted geographic points.

Time- and Space-Efficient Aggregate Range Queries over Encrypted Databases

We present ARQ, a systematic framework for creating cryptographic schemes that handle range aggregate queries (sum, minimum, median, and mode) over encrypted datasets. Our framework does not rely on trusted hardware or specialized cryptographic primitives such as property-preserving or homomorphic encryption. Instead, ARQ unifies structures from the plaintext data management community with existing structured encryption primitives. We prove how such combinations yield efficient (and secure) constructions in the encrypted setting. We also propose a series of domain reduction techniques that can improve the space efficiency of our schemes against sparse datasets at the cost of small leakage. As part of this work, we designed and implemented a new, open-source, encrypted search library called Arca and implemented the ARQ framework using this library in order to evaluate ARQ’s practicality. Our experiments on real-world datasets demonstrate the efficiency of the schemes derived from ARQ in comparison to prior work.

A Hybrid Privacy-Preserving Deep Learning Approach for Object Classification in Very High-Resolution Satellite Images

Deep learning (DL) has shown outstanding performances in many fields, including remote sensing (RS). DL is turning into an essential tool for the RS research community. Recently, many cloud platforms have been developed to provide access to large-scale computing capacity, consequently permitting the usage of DL architectures as a service. However, this opened the door to new challenges associated with the privacy and security of data. The RS data used to train the DL algorithms have several privacy requirements. Some of them need a high level of confidentiality, such as satellite images related to public security with high spatial resolutions. Moreover, satellite images are usually protected by copyright, and the owner may strictly refuse to share them. Therefore, privacy-preserving deep learning (PPDL) techniques are a possible solution to this problem. PPDL enables training DL on encrypted data without revealing the original plaintext. This study proposes a hybrid PPDL approach for object classification for very-high-resolution satellite images. The proposed encryption scheme combines Paillier homomorphic encryption (PHE) and somewhat homomorphic encryption (SHE). This combination aims to enhance the encryption of satellite images while ensuring a good runtime and high object classification accuracy. The method proposed to encrypt images is maintained through the public keys of PHE and SHE. Experiments were conducted on real-world high-resolution satellite images acquired using the SPOT6 and SPOT7 satellites. Four different CNN architectures were considered, namely ResNet50, InceptionV3, DenseNet169, and MobileNetV2. The results showed that the loss in classification accuracy after applying the proposed encryption algorithm ranges from 2% to 3.5%, with the best validation accuracy on the encrypted dataset reaching 92%.

A Fusion-Denoising Attack on InstaHide with Data Augmentation

InstaHide is a state-of-the-art mechanism for protecting private training images, by mixing multiple private images and modifying them such that their visual features are indistinguishable to the naked eye. In recent work, however, Carlini et al. show that it is possible to reconstruct private images from the encrypted dataset generated by InstaHide. Nevertheless, we demonstrate that Carlini et al.’s attack can be easily defeated by incorporating data augmentation into InstaHide. This leads to a natural question: is InstaHide with data augmentation secure? In this paper, we provide a negative answer to this question, by devising an attack for recovering private images from the outputs of InstaHide even when data augmentation is present. The basic idea is to use a comparative network to identify encrypted images that are likely to correspond to the same private image, and then employ a fusion-denoising network for restoring the private image from the encrypted ones, taking into account the effects of data augmentation. Extensive experiments demonstrate the effectiveness of the proposed attack in comparison to Carlini et al.’s attack.

Web-based nautical charts automated compilation from open hydrospatial data

AbstractElectronic navigational charts (ENCs) are specialised geospatial datasets, issued by or on the authority of a government or hydrographic office, in accordance with the International Hydrographic Organisation's (IHO) standards, specifications and symbol sets. The datasets generally comprise encoded information collected from hydrographic surveys, aimed primarily at the safety of navigation. Most ENCs are not openly available, since the encrypted datasets can be acquired through various license schemes via a centralised distribution network coordinated by two organisations operating on behalf of the coastal states that produce them. This paper describes a methodology and an integrated system developed at the National Technical University of Athens Cartography Laboratory for the generation of web-based nautical charts utilising open data and free software. The system compiles nautical charts compliant with IHO's S-101 latest standard; using open hydrospatial data retrieved from marine spatial data infrastructures (MSDI) and other qualified volunteered geographic information (VGI) sources. Open-source geospatial libraries and web-map vector technologies are used to build the system components and software scripts developed to enable automated compilation. The study also discusses how the system can be improved further by leveraging web services for end-to-end process automation and satellite-derived bathymetry for accurate depiction of seabed topography in low-depth areas.

A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning

One of the foundational and key means of optimizing network service in the field of network security is traffic identification. Various data transmission encryption technologies have been widely employed in recent years. Wrongdoers usually bypass the defense of network security facilities through VPN to carry out network intrusion and malicious attacks. The existing encrypted traffic identification system faces a severe problem as a result of this phenomenon. Previous encrypted traffic identification methods suffer from feature redundancy, data class imbalance, and low identification rate. To address these three problems, this paper proposes a VPN-encrypted traffic identification method based on ensemble learning. Firstly, aiming at the problem of feature redundancy in VPN-encrypted traffic features, a method of selecting encrypted traffic features based on mRMR is proposed; secondly, aiming at the problem of data class imbalance, improving the Xgboost identification model by using the focal loss function for the data class imbalance problem; Finally, in order to improve the identification rate of VPN-encrypted traffic identification methods, an ensemble learning model parameter optimization method based on optimal Bayesian is proposed. Experiments revealed that our proposed VPN-encrypted traffic identification method produced more desirable VPN-encrypted traffic identification outcomes. Meanwhile, using two encrypted traffic datasets, eight common identification algorithms are compared, and the method appears to be more accurate in identifying encrypted traffic.

EAFS: An Efficient, Accurate, and Forward Secure Searchable Encryption Scheme Supporting Range Search

Forward privacy in existing searchable symmetric encryption approaches generally supports only keyword searches, rather than range searches. In addition, existing approaches that support range search over encrypted datasets have limitations associated with efficiency, accuracy, and security. Seeking to address these limitations, we formally propose the definition of the forward privacy for secure range searches. Also, we demonstrate how three widely used cryptographic tools&#x2014; order-preserving encryption (OPE), pseudorandom function, and one-time pad&#x2013;can be leveraged to design an efficient, accurate, and forward secure (EAFS) searchable encryption scheme supporting range search in encrypted numerical databases. In the proposed EAFS scheme, a trapdoor only matches the last data record that satisfies the search range, and the other results are found iteratively using the previous result. The chain-like search and the embedded ciphertexts of OPE simultaneously guarantee its efficiency, forward privacy, and accuracy. We also use the simulation-based method to demonstrate that our scheme is secure. Then, we implement EAFS in Microsoft Azure and evaluate its performance. The evaluation findings demonstrate that our proposed scheme supports secure range search practically and efficiently.

Toward practical privacy-preserving linear regression

Linear regression is an ordinary machine learning algorithm that models the relation between the input values and the output ones with underlying linear functions. Giacomelli et al. (ACNS 2018) proposed the first system training the linear regression model over the rational numbers using only linearly homomorphic encryption. However, we find their system model is not applicable. A third authority generates the public key and secret key, which are used to encrypt and decrypt all the data sets. Then the privacy of data sets is in the risk of leakage even if the third authority is assumed to have no access to encrypted data sets. In this paper, we improve the system model in order to design a more practical linear regression algorithm over the rational numbers from the view of security. Concretely, every data owner generates his own public key and secret key, independent on a third authority. An improved multi-key fully homomorphic encryption over complex numbers is utilized to construct our linear regression algorithm with a preprocessing phase, which can directly encrypt rational numbers, support computations over ciphertexts under multi keys and obviate the rational reconstruction technique as Giacomelli et al.. Furthermore, performance analyses demonstrate that our algorithm is more feasible and practical.

Transparent Ciphertext Retrieval System Supporting Integration of Encrypted Heterogeneous Database in Cloud-Assisted IoT

Ciphertext retrieval under heterogeneous data sets is a critical concern in the cloud-assisted Internet of Things (IoT). Previous ciphertext retrieval schemes eliminate computation burden and security concerns to some extent, but cannot support the encrypted heterogeneous data sets. To solve this problem, in this article we propose a transparent ciphertext retrieval system regardless of programming language, accessible platforms, and heterogeneity of data sets. The retrieval system is a novel combination of the proposed access and integration middleware with a current encrypted cloud database system. The middleware enables cross-language and cross-platform queries over various database systems, besides, it schedules the designed data integration method in heterogeneous databases to realize cross-database queries over the encrypted cloud data sets. The proposed ciphertext retrieval system achieves efficient data sharing among different IoT applications with various database systems. We prove that the system guarantees data security by avoiding malicious hackers. Extensive experiments show that our design is efficient and feasible in practice.