Level Of Obfuscation Research Articles

Location Privacy Protection in Edge Computing: Co-Design of Differential Privacy and Offloading Mode

Edge computing has emerged as an innovative paradigm that decentralizes computation to the network’s periphery, empowering edge servers to manage user-initiated complex tasks. This strategy alleviates the computational load on end-user devices and increases task processing efficiency. Nonetheless, the task offloading process can introduce a critical vulnerability, as adversaries may infer a user’s location through an analysis of their offloading mode, thereby threatening the user’s location privacy. To counteract this vulnerability, this study introduces differential privacy as a protective mechanism to obscure the user’s offloading mode, thereby safeguarding their location information. This research specifically addresses the issue of location privacy leakage stemming from the correlation between a user’s location and their task offloading ratio. The proposed strategy is based on differential privacy. It aims to increase the efficiency of offloading services and the benefits of task offloading. At the same time, it ensures privacy protection. An innovative optimization technique for task offloading that maintains location privacy is presented. Utilizing this technique, users can make informed offloading decisions, dynamically adjusting the level of obfuscation in response to the state of the wireless channel and their privacy requirements. This study substantiates the feasibility and effectiveness of the proposed mechanism through rigorous theoretical analysis and extensive empirical testing. The numerical results demonstrate that the proposed strategy can achieve a balance between offloading privacy and processing overhead.

MAN[sbnd]C: A masked autoencoder neural cryptography based encryption scheme for CT scan images

Sharing medical images securely is very important towards keeping patients’ data confidential. In this paper we propose MANC: a Masked Autoencoder Neural Cryptography based encryption scheme for sharing medical images. The proposed technique builds upon recently proposed masked autoencoders. In the original paper, the masked autoencoders are used as scalable self-supervised learners for computer vision which reconstruct portions of originally patched images. Here, the facility to obfuscate portions of input image and the ability to reconstruct original images is used an encryption-decryption scheme. In the final form, masked autoencoders are combined with neural cryptography consisting of a tree parity machine and Shamir Scheme for secret image sharing. The proposed technique MANC helps to recover the loss in image due to noise during secret sharing of image.•Uses recently proposed masked autoencoders, originally designed as scalable self-supervised learners for computer vision, in an encryption-decryption setup.•Combines autoencoders with neural cryptography - the advantage our proposed approach offers over existing technique is that (i) Neural cryptography is a new type of public key cryptography that is not based on number theory, requires less computing time and memory and is non-deterministic in nature, (ii) masked auto-encoders provide additional level of obfuscation through their deep learning architecture.•The proposed scheme was evaluated on dataset consisting of CT scans made public by The Cancer Imaging Archive (TCIA). The proposed method produces better RMSE values between the input the encrypted image and comparable correlation values between the input and the output image with respect to the existing techniques.

Linear Feedback Shift Registers-Based Randomization for Image Steganography

Steganography involves concealing information by embedding data within cover media and it can be categorized into two main domains: spatial and frequency. This paper presents two distinct methods. The first is operating in the spatial domain which utilizes the least significant bits (LSBs) to conceal a secret message. The second method is the functioning in the frequency domain which hides the secret message within the LSBs of the middle-frequency band of the discrete cosine transform (DCT) coefficients. These methods enhance obfuscation by utilizing two layers of randomness: random pixel embedding and random bit embedding within each pixel. Unlike other available methods that embed data in sequential order with a fixed amount. These methods embed the data in a random location with a random amount, further enhancing the level of obfuscation. A pseudo-random binary key that is generated through a nonlinear combination of eight Linear Feedback Shift Registers (LFSRs) controls this randomness. The experimentation involves various 512x512 cover images. The first method achieves an average PSNR of 43.5292 with a payload capacity of up to 16% of the cover image. In contrast, the second method yields an average PSNR of 38.4092 with a payload capacity of up to 8%. The performance analysis demonstrates that the LSB-based method can conceal more data with less visibility, however, it is vulnerable to simple image manipulation. On the other hand, the DCT-based method offers lower capacity with increased visibility, but it is more robust.

A Novel Traffic Obfuscation Technology for Smart Home

With the widespread popularity of smart home devices and the emergence of smart home integration platforms such as Google, Amazon, and Xiaomi, the smart home industry is in a stage of vigorous development. While smart homes provide users with convenient and intelligent living, the problem of smart home devices leaking user privacy has become increasingly prominent. Smart home devices give users the ability to remotely control home devices, but they also reflect user home activities in traffic data, which brings the risk of privacy leaks. Potential attackers can use traffic classification technology to analyze traffic characteristics during traffic transmission (e.g., at the traffic exit of a smart home gateway) and infer users’ private information, such as their home activities, causing serious consequences of privacy leaks. To address the above problems, this paper focuses on research on privacy protection technology based on traffic obfuscation. By using traffic obfuscation technology to obscure the true traffic of smart home devices, it can prevent malicious traffic listeners from analyzing user privacy information based on traffic characteristics. We propose an enhanced smart home traffic obfuscation method called SHTObfuscator (Smart Home Traffic Obfuscator) based on the virtual user technology concept and a virtual user behavior construction method based on logical integrity. By injecting traffic fingerprints of different device activities into the real traffic environment of smart homes as obfuscating traffic, attackers cannot distinguish between the real device working status and user behavior privacy in the current home, effectively reducing the effect of traffic classification attack models. The protection level can be manually or automatically adjusted, achieving a balance between privacy protection and bandwidth overhead. The experimental results show that under the highest obfuscation level, the obfuscation method proposed in this paper can effectively reduce the classification effect of the attack model from 95% to 25%.

A dynamic method to protect user privacy against traffic-based attacks on smart home

The Internet of Things (IoT) has revolutionized how people interact with their living spaces. However, attackers can perform traffic-based attacks to reveal the behavior of legitimate users, seriously compromising their privacy. Studies have proposed to obfuscate network traffic to avoid these attacks. However, there is still the challenge of ensuring a trade-off between privacy and network overhead. This work introduces a Method for IoT Network Traffic Obfuscation (MITRA) that protects user privacy in smart homes while keeping IoT network overhead low. The method relies on the dummy traffic injection following different levels of obfuscation. The different levels mask network traffic, improving privacy without harming network performance unnecessarily. Results show that the obfuscation of IoT device traffic reduces traffic identification accuracy by up to 42%.

Secure and Energy-Efficient Network Topology Obfuscation for Software-Defined WSNs

Network topology obfuscation (NTO) is generally considered as a promising proactive mechanism to mitigate traffic analysis attacks. The main challenge is to strike a balance among energy consumption, reliable routing, and security levels due to resource constraints in sensor nodes. Furthermore, software-defined wireless sensor networks (WSNs) are more vulnerable to traffic analysis attacks due to the uncovered pattern of control traffic between the controller and the nodes. In this article, a new energy-aware NTO mechanism is proposed, which maximizes the attack costs and is efficient and practical to be deployed. Specifically, first, a route obfuscation method is proposed by utilizing ranking-based route mutation, based on four different critical criteria: 1) route overlapping; 2) energy consumption; 3) link costs; and 4) node reliability. Then, a sink node obfuscation method is introduced by selecting several fake sink nodes that are indistinguishable from actual sink nodes, according to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -anonymity model. As a result, the most suitable routes and sink nodes can be selected, and a highest obfuscation level can be reached without sacrificing energy efficiency. Finally, extensive simulation results demonstrate that the proposed methods can strongly mitigate traffic analysis attacks and achieve effective NTO for software-defined WSNs. In addition, the proposed methods can reduce the success rate of the attacks while achieving lower energy consumption and higher network lifetime.

Annual Reports Readability From Linguistic and Communication Perspectives: Systematic Literature Review

This research presents a new theoretical framework through assessing readability research based on the linguistics and communication perspectives to determine the obfuscation probabilities and how to mitigate them. Therefore, this systematic literature review analyzed 219 papers using the SCOPUS and Web of Science databases. Findings show that in every language approach, there is an obfuscation level for annual reports, depending on the weakness of a particular component of the text communication process, starting from the use of a complex writing style and ending with the imposition of specific methods of presentation, while suggesting ways to mitigate the obfuscation.

Now You See It, Now You Don’t: Obfuscation of Online Third-Party Information Sharing

The practice of sharing online user information with external third parties has become the focal point of privacy concerns for consumer advocacy groups and policy makers. We explore the decisions by websites regarding the obfuscation that they use to make it difficult for users to discover the extent of information sharing. Using a Bayesian model, we shed light on the websites’ incentive to obfuscate user information sharing. We find that as content sensitivity increases, a website reduces its level of obfuscation. Furthermore, more popular websites engage in higher levels of obfuscation than less popular ones. We provide an empirical analysis of obfuscation and user information sharing in News (low content sensitivity) and Health (high content sensitivity) websites and confirm key results from our analytical model. Our analysis illustrates that obfuscation of information sharing is a viable strategy that websites use to improve their profits. History: Ram Ramesh, area editor for Data Science &amp; Machine Learning. Funding: Financial support from the Social Sciences and Humanities Research Council of Canada is gratefully acknowledged. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1266 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0070 ) at http://dx.doi.org/10.5281/zenodo.7336098 .

Reconfigurable Field Effect Transistors Design Solutions for Delay-Invariant Logic Gates

Reconfigurable field effect transistors (RFETs) are an emerging technology platform that offers the possibility to merge <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> -type and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> -type functionalities in a single device. From the circuit perspective, this feature enables layout camouflaged designs by realizing polymorphic logic gates with dynamically reconfigurable functions. In this work, mixed-mode simulations employing a technology computer-aided design (TCAD) model of RFETs with three gates are presented. Three different designs for reconfigurable NAND/NOR logic gates are analyzed in order to optimize the equalization of both operational modes delay traces. Moreover, work function fluctuations arising from process variations are considered to prove that their inevitable presence can be exploited to further increase the level of obfuscation between those modes. Statistical analysis of the results from 100 simulated devices shows effective overlapping of the distribution of the delays extracted at half the value of the drain voltage. Together with a good resilience against supply voltage variations in fault induction attacks schemes, these results suggest how this emerging technology can grow up and evolve to play an interesting role in the field of hardware security.

A Multi-Criteria Allocation Strategy for Provisioning Cloud Resources

The adoption of the Business Process outsourcing strategy has been accentuated with the advent of Cloud computing that brings a new model for consuming and paying resources. Nevertheless, companies hesitate between a high distribution allowing to combine several Cloud offers adapted for process fragments and guarantee a good level of obfuscation or a weak distribution to minimize the data transfer which is charged by most suppliers and which may slow down the execution of processes. Hence, this paper proposes a novel selection method based on a heuristic algorithm that aims to find the right balance by optimizing the cost and performance while ensuring the protection of outsourced processes. To this end, several criteria are considered such as data transfer, task separation constraints and tolerated delay margins. The results obtained through a case study and comparisons with other selection methods show that the configurations selected by the algorithm meet companies’ requirements and are at the same time inexpensive, efficient and secure.

Eight Years of Rider Measurement in the Android Malware Ecosystem

Despite the growing threat posed by the Android malware, the research community is still lacking a comprehensive view of common behaviors and emerging trends in malware families active on the platform. Without such view, researchers incur the risk of developing systems that only detect outdated threats, missing the most recent ones. In this article, we conduct the largest measurement of Android malware behavior to date, analyzing over 1.2 million malware samples that belong to 1.28K families over a period of eight years (from 2010 to 2017). We aim at understanding how Android malware has evolved over time, focusing on <i>repackaging</i> malware. In this type of threat different innocuous apps are piggybacked with a malicious payload (<i>rider</i>), allowing inexpensive malware manufacturing. One of the main challenges posed when studying repackaged malware is slicing the app to split benign components apart from the malicious ones. To address this problem, we use differential analysis to isolate software components that are irrelevant to the campaign and study the behavior of malicious riders alone. Our analysis framework relies on collective repositories and recent advances on the systematization of intelligence extracted from multiple anti-virus vendors. We find that since its infancy in 2010, the Android malware ecosystem has changed significantly, both in the type of malicious activity performed by malware and in the level of obfuscation used to avoid detection. Finally, we discuss what our findings mean for Android malware detection research, highlighting areas that need further attention by the research community. In particular, we show that riders of malware families evolve over time. This evidences important experimental bias in research works levering on automated systems for family identification without considering variants.

Multi-Group ObScure Logging (MG-OSLo) A Privacy-Preserving Protocol for Private Web Search

The Web Search Engine (WSE) is a software system used to retrieve data from the web successfully. WSE uses the user’s search queries to build the user’s profile and provide personalized results. Users’ search queries hold identifiable information that could compromise the privacy of the respective user. This work proposes a multi-group distributed privacy-preserving protocol (MG-OSLo) and tries to investigate the state-of-the-art distributed privacy-preserving protocols for computing web search privacy. The MG-OSLo comprises multiple groups in which each group has a fixed number of users. The MG-OSLo measures the impact of the multi-group on the user’s privacy. The primary objective of this work is to assess local privacy and profile privacy. It aims at evaluating the impact of group size and group count on a user’s privacy. Two grouping approaches are used to group the users in MG-OSLo, i.e. a non-overlapping group design and overlapping group design. The local privacy results reveal that the probability of linking a query to the user depends on the group size and group count. The higher the group size or group count, the lower the likelihood of relating the query to the user. The profile privacy computes the profile obfuscation level using a privacy metric Profile Exposure Level (PEL). Different experiments have been performed to compute the profile privacy of the subset of an AOL query log for two situations: i) self-query submissions allowed and ii) self-query submissions not allowed. The privacy achieved by MG-OSLo is compared with the modern privacy-preserving protocol UUP(e), OSLo, and Co-utile protocols. The results show that the MG-OSLo provided better results as compared to OSLo, UUP, and Co-utile. Similarly, the multi-group has a positive impact on local privacy and user profile privacy.

The Interplay Between Obfuscation and Prominence in Price Comparison Platforms

This paper analyzes price comparison platforms’ equilibrium design of their search environments, namely the order under which firms are displayed (having a prominent firm or listing firms randomly) and how much price complexity firms are able to use. It is found that (1) the possibility to obfuscate amplifies firms’ willingness to pay for the prominent position; (2) when platforms sell prominence, they optimally allow for more obfuscation than they would if they were to display firms randomly; and (3) if platforms are sufficiently differentiated, they find it optimal to sell prominence. Thus, platforms exploit their market power over consumers by implementing a prominent position and allowing for large levels of obfuscation. This paper also finds that in equilibrium, there is tension between platforms and firms regarding how much price complexity is used: firms would like to use even more obfuscation than what the platform allows, so the platform must monitor firms’ prices to make sure that they are not excessively complex. This paper was accepted by Juanjuan Zhang, marketing.

Optimal Design of Revenue-Maximizing Position Auctions With Consumer Search

We examine the impact of consumer search from the perspective of revenue-maximizing optimal auction design. In this two-sided market, advertisers are endowed with privately known qualities, and consumers incur heterogeneous search costs when clicking on advertisements displayed by the search engine. Our general approach eliminates ad hoc restrictions and therefore allows us to uncover the salient economic trade-offs. We adopt the optimal mechanism design approach to examine this two-sided private information problem. We show that from the revenue maximization perspective, the search engine necessarily displays the advertisers in the descending order of their qualities. The search engine shall insert unnecessary hassles to induce the revenue-maximizing search behaviors. Thus, each consumer stops searching pre-maturely compared to the socially optimal search behavior. Moreover, the obfuscation level is higher for consumers with higher search costs. In addition, the search engine shall implement probabilistic forced termination upon consumers' clicking on advertisements, and this probability is higher for advertisers with poorer qualities. When each advertiser has a two-dimensional type on consumer learning and matching, we show that even if consumers' search costs become negligible, the allocative inefficiency does not vanish. Moreover, no strategic obfuscation is imposed. When allowing for non-independent qualities, we show that product variety does not constitute a rationale for advertisers' favoritism.

Call graph obfuscation and diversification: an approach

Monetary loss due to software piracy nowadays reaches millions. In 2017, the commercial value for this concept rose to $46.3 billion. A way to mitigate this problem from the technological point of view is the use of software protection techniques, especially the obfuscation and diversification of code, highlighting the control obfuscation. There are many proposals connected with obfuscating control flow graph. However, there are few reported works that perform obfuscation of the call graph. In this study, the authors propose a novel mechanism for the static obfuscation and diversification of the call graph of a software. The mechanism is based on the routing of functions calls in order to modify the software call graph. A prototype of the proposed mechanism was developed by extending the functionalities of a compiler. The generated software differed structurally by 25% on average, compared to the original software. There was an increase in the level of obfuscation from 2 to 30% in the tests performed, with only a 3% overhead of the execution time in all cases. The proposal allows to restructure the whole call graph efficiently, increasing the level of protection without affecting significantly the software performance.

Secure Routing Protocols for Source Node Privacy Protection in Multi-Hop Communication Wireless Networks

Traffic analysis attacks are common in monitoring wireless sensor networks (WSNs). In the attacks, adversaries analyze the traffic pattern to obtain critical information such as the location information of a source node. Fake source packet routing protocols are often used to ensure source location privacy (SLP) protection. The protocols rely on broadcasting fake packets from fake sources concurrently with the transmission of real packets from the real source nodes to obfuscate the adversaries. However, fake source packet routing protocols have demonstrated some performance limitations including high energy consumption, low packet delivery ratio (PDR), and long end-to-end delay (EED). In this study, two existing fake source packet routing protocols are considered. Then two new phantom-based SLP routing protocols are proposed to address the limitations. Each proposed protocol introduces a two-level phantom routing strategy to ensure two adversary confusion phases. When the adversaries perform traffic analysis attacks on the packet routes, they encounter two levels of obfuscation. Simulation results establish that the proposed protocols have superior performance features. The protocols guarantee strong SLP protection throughout the WSN domain with controlled energy consumption, PDR, and EED. Furthermore, the proposed protocols achieve more practical results under varied network configurations.

Protecting Private Attributes in App Based Mobile User Profiling

The Analytics companies enable successful targeted advertising via user profiles, derived from the mobile apps installed by specific users, and hence have become an integral part of the mobile advertising industry. This threatens the users' privacy, when profiling is based on apps representing sensitive information, e.g., gambling problems indicated by a game app. In this work, we propose an app-based profile obfuscation mechanism, ProfileGuard, with the objective of eliminating the dominance of private interest categories (i.e. the prevailing private interest categories present in a user profile). We demonstrate, based on wide-range experimental evaluation of Android apps in a nine month test campaign, that the proposed obfuscation mechanism based on similarity with user's existing apps (ensuring that selected obfuscating apps belong to non-private categories) can achieve a good trade-off between efforts required by the obfuscating system and the resulting privacy protection. We also show how the bespoke (customised to profile obfuscation) and bespoke++ (resource-aware) strategies can deliver significant improvements in the level of obfuscation and (particularly bespoke++) in the use of mobile resources, making the latter a good candidate strategy in resource-constrained scenarios e.g., for fixed data use mobile plans. We also implement a POC ProfileGuard app to demonstrate the feasibility of an automated obfuscation mechanism. Furthermore, we provide insights to Google AdMob profiling rules, such as showing how individual apps map to user's interests within their profile in a deterministic way and that AdMob requires a certain level of activity to build a stable user profile.

Scalable and language-independent embedding-based approach for plagiarism detection considering obfuscation type: no training phase

The efficiency and scalability of plagiarism detection systems have become a major challenge due to the vast amount of available textual data in several languages over the Internet. Plagiarism occurs in different levels of obfuscation, ranging from the exact copy of original materials to text summarization. Consequently, designed algorithms to detect plagiarism should be robust to the diverse languages and different types of obfuscation in plagiarism cases. In this paper, we employ text embedding vectors to compare similarity among documents to detect plagiarism. Word vectors are combined by a simple aggregation function to represent a text document. This representation comprises semantic and syntactic information of the text and leads to efficient text alignment among suspicious and original documents. By comparing representations of sentences in source and suspicious documents, pair sentences with the highest similarity are considered as the candidates or seeds of plagiarism cases. To filter and merge these seeds, a set of parameters, including Jaccard similarity and merging threshold, are tuned by two different approaches: offline tuning and online tuning. The offline method, which is used as the benchmark, regulates a unique set of parameters for all types of plagiarism by several trials on the training corpus. Experiments show improvements in performance by considering obfuscation type during threshold tuning. In this regard, our proposed online approach uses two statistical methods to filter outlier candidates automatically by their scale of obfuscation. By employing the online tuning approach, no distinct training dataset is required to train the system. We applied our proposed method on available datasets in English, Persian and Arabic languages on the text alignment task to evaluate the robustness of the proposed methods from the language perspective as well. As our experimental results confirm, our efficient approach can achieve considerable performance on the different datasets in various languages. Our online threshold tuning approach without any training datasets works as well as, or even in some cases better than, the training-base method.

Defeating Silicon Reverse Engineering Using a Layout-Level Standard Cell Camouflage

Intellectual property protection techniques face a challenging task in countering a physical attack by reverse engineering the netlist of an embedded integrated circuit. An attacker can extract sensitive information with image tools by processing microphotographs at low metal layers of delayered chips. We propose a low performance and zero-area impact method to obfuscate circuits by using a current digital design flow with a layout standard cell generator to obtain different physical versions of the same logic cell. Results indicate that timing and power overhead, introduced by the obfuscation method, can be mitigated. After applying the method to a set of benchmark circuits and a 32-bit RISC-based microprocessor, results show a 40%–50% average obfuscation with zero area penalty and less than 2% timing and power penalty for system level blocks. Considering that most attacks direct reverse engineering to key cryptographic functions, experimental obfuscation results indicate a timing penalty of 4% with a strong obfuscation level for a synthesized key establishment core.

Mathematical Programming Algorithms for Spatial Cloaking

We consider a combinatorial optimization problem for spatial information cloaking. The problem requires computing one or several disjoint arborescences on a graph from a predetermined root or subset of candidate roots, so that the number of vertices in the arborescences is minimized but a given threshold on the overall weight associated with the vertices in each arborescence is reached. For a single arborescence case, we solve the problem to optimality by designing a branch-and-cut exact algorithm. Then we adapt this algorithm for the purpose of pricing out columns in an exact branch-and-price algorithm for the multiarborescence version. We also propose a branch-and-price-based heuristic algorithm, where branching and pricing, respectively, act as diversification and intensification mechanisms. The heuristic consistently finds optimal or near optimal solutions within a computing time, which can be three to four orders of magnitude smaller than that required for exact optimization. From an application point of view, our computational results are useful to calibrate the values of relevant parameters, determining the obfuscation level that is achieved. The online supplement is available at https://doi.org/10.1287/ijoc.2018.0813 .