Privacy-sensitive Information Research Articles

Context-Rich Privacy Leakage Analysis Through Inferring Apps in Smart Home IoT

Emerging Internet of Things (IoT) systems leverage connected devices to enable intelligent and automated functionalities. Despite the benefits, there exist privacy risks of network traffic, which have been studied by the previous research. However, with the current privacy inference remaining at the event-level, potential privacy risks are underestimated, which, as our study shows, can be much higher than previously reported through app-level traffic analysis. A key observation of our research is that IoT event-triggered traffic is generated by apps, which often adopt an if-trigger-then-action (trigger-action) programming paradigm. We utilize this feature to develop fingerprints to differentiate running apps and learn context-rich privacy-sensitive information from apps. In this article, we present a privacy leakage analysis called ALTA to infer running apps in smart home IoT environments. First, ALTA identifies app fingerprints through static analysis and extracts sensitive information from app descriptions and input prompts. Then, through dynamic traffic profiling, it learns traffic fingerprints of apps. Finally, ALTA matches the fingerprints of app and traffic, and thus is able to pinpoint which app is running from IoT traffic at runtime. To demonstrate the feasibility of our approach, we analyze 254 SmartThings applications via program and natural language processing (NLP) analysis. We also perform the app inference evaluation on 31 apps executed in a simulated smart home. The results suggest that ALTA can effectively infer running apps from IoT traffic and learn context-rich information (e.g., health conditions, daily routines, and user activities) from apps with high accuracy.

Enabling Fine-Grained Multi-Keyword Search Supporting Classified Sub-Dictionaries Over Encrypted Cloud Data


 
 
 
 Abstract
 Huge numbers of papers are transferred to the cloud for easier access and lower administration costs as cloud computing gains popularity. Individuals can utilize cloud computing to store their data on distant servers and grant public users access to the data via the cloud servers. Outsourced data are normally encrypted before being posted to the clod since they are likely to include sensitive privacy information. The difficulty of sifting through the encrypted data, however, severely restricts the utility of the outsourced data. In this research, we introduce the fine-grained multi-keyword search algorithms over encrypted clod data to overcome this problem. We made three original contributions. In order to provide exact keyword search and a tailored user experience, we initially present the relevance source plus preference factor upon keywords. Furthermore, we create a useful and very effective multi-keyword search strategy. The suggested system can enable complex logic searches using mixed "AND," "OR," and "NO" keyword operations. Thirdly, in order to improve the efficiency of index building and query generation, we also use the classified sub-dictionaries technique. Finally, we examine the security of the suggested strategies with regard to of document confidentiality, index alongside trapdoor privacy protection, as well as trapdoor unlinkability. We confirm that the suggested method performs better when it comes to of functionality, query complexity, yet efficiency than the ones currently in use plus can reach the same security level through rigorous tests on a real-world dataset. We confirm that the suggested method performs better with regard to functionality, query complexity, and efficiency than the existing ones and can reach the same security level through rigorous tests on a real-world dataset.
 
 
 

On the non-intrusive extraction of residents’ privacy- and security-sensitive information from energy smart meters

Energy smart meters have become very popular in monitoring and smart energy management applications. However, the acquired measurements except the energy consumption information may also carry information about the residents’ daily routine, preferences and profile. In this article, we investigate the potential of extracting information from smart meters related to residents’ security- and privacy-sensitive information. Specifically, using methodologies for load demand prediction, non-intrusive load monitoring and elastic matching, evaluation of extraction of information related to house occupancy, multimedia watching detection, socioeconomic and health profiling of residents was performed. The evaluation results showed that the aggregated energy consumption signals contain information related to residents’ privacy and security, which can be extracted from the smart meter measurements.

Development of a Privacy-Preserving UAV System With Deep Learning-Based Face Anonymization

The robot market has been growing significantly and is expected to become 1.5 times larger in 2024 than what it was in 2019. Robots have attracted attention of security companies thanks to their mobility. These days, for security robots, unmanned aerial vehicles (UAVs) have quickly emerged by highlighting their advantage: they can even go to any hazardous place that humans cannot access. For UAVs, Drone has been a representative model and has several merits to consist of various sensors such as high-resolution cameras. Therefore, Drone is the most suitable as a mobile surveillance robot. These attractive advantages such as high-resolution cameras and mobility can be a double-edged sword, i.e., privacy infringement. Surveillance drones take videos with high-resolution to fulfill their role, however, those contain a lot of privacy sensitive information. The indiscriminate shooting is a critical issue for those who are very reluctant to be exposed. To tackle the privacy infringement, this work proposes face-anonymizing drone patrol system. In this system, one person's face in a video is transformed into a different face with facial components maintained. To construct our privacy-preserving system, we have adopted the latest generative adversarial networks frameworks and have some modifications on losses of those frameworks. Our face-anonymzing approach is evaluated with various public face-image and video dataset. Moreover, our system is evaluated with a customized drone consisting of a high-resolution camera, a companion computer, and a drone control computer. Finally, we confirm that our system can protect privacy sensitive information with our face-anonymzing algorithm while preserving the performance of robot perception, i.e., simultaneous localization and mapping.

Challenges in Contact Tracing by Mining Mobile Phone Location Data for COVID-19: Implications for Public Governance in South Africa

Aim/Purpose: The paper’s objective is to examine the challenges of using the mobile phone to mine location data for effective contact tracing of symptomatic, pre-symptomatic, and asymptomatic individuals and the implications of this technology for public health governance. Background: The COVID-19 crisis has created an unprecedented need for contact tracing across South Africa, requiring thousands of people to be traced and their details captured in government health databases as part of public health efforts aimed at breaking the chains of transmission. Contact tracing for COVID-19 requires the identification of persons who may have been exposed to the virus and following them up daily for 14 days from the last point of exposure. Mining mobile phone location data can play a critical role in locating people from the time they were identified as contacts to the time they access medical assistance. In this case, it aids data flow to various databases designated for COVID-19 work. Methodology: The researchers conducted a review of the available literature on this subject drawing from academic articles published in peer-reviewed journals, research reports, and other relevant national and international government documents reporting on public health and COVID-19. Document analysis was used as the primary research method, drawing on the case studies. Contribution: Contact tracing remains a critical strategy in curbing the deadly COVID-19 pandemic in South Africa and elsewhere in the world. However, given increasing concern regarding its invasive nature and possible infringement of individual liberties, it is imperative to interrogate the challenges related to its implementation to ensure a balance with public governance. The research findings can thus be used to inform policies and practices associated with contact tracing in South Africa. Findings: The study found that contact tracing using mobile phone location data mining can be used to enforce quarantine measures such as lockdowns aimed at mitigating a public health emergency such as COVID-19. However, the use of technology can expose the public to criminal activities by exposing their locations. From a public governance point of view, any exposure of the public to social ills is highly undesirable. Recommendations for Practitioners: In using contact tracing apps to provide pertinent data location caution needs to be exercised to ensure that sensitive private information is not made public to the extent that it compromises citizens’ safety and security. The study recommends the development and implementation of data use protocols to support the use of this technology, in order to mitigate against infringement of individual privacy and other civil liberties. Recommendation for Researchers: Researchers should explore ways of improving digital applications in order to improve the acceptability of the use of contact tracing technology to manage pandemics such as COVID-19, paying attention to ethical considerations. Impact on Society: Since contact tracing has implications for privacy and confidentiality it must be conducted with caution. This research highlights the challenges that the authorities must address to ensure that the right to privacy and confidentiality is upheld. Future Research: Future research could focus on collecting primary data to provide insight on contact tracing through mining mobile phone location data. Research could also be conducted on how app-based technology can enhance the effectiveness of contact tracing in order to optimize testing and tracing coverage. This has the potential to minimize transmission whilst also minimizing tracing delays. Moreover, it is important to develop contact tracing apps that are universally inter-operable and privacy-preserving.

Spatio-Temporal Split Learning for Privacy-Preserving Medical Platforms: Case Studies With COVID-19 CT, X-Ray, and Cholesterol Data

Machine learning requires a large volume of sample data, especially when it is used in high-accuracy medical applications. However, patient records are one of the most sensitive private information that is not usually shared among institutes. This paper presents spatio-temporal split learning, a distributed deep neural network framework, which is a turning point in allowing collaboration among privacy-sensitive organizations. Our spatio-temporal split learning presents how distributed machine learning can be efficiently conducted with minimal privacy concerns. The proposed split learning consists of a number of clients and a centralized server. Each client has only has one hidden layer, which acts as the privacy-preserving layer, and the centralized server comprises the other hidden layers and the output layer. Since the centralized server does not need to access the training data and trains the deep neural network with parameters received from the privacy-preserving layer, privacy of original data is guaranteed. We have coined the term, spatio-temporal split learning, as multiple clients are spatially distributed to cover diverse datasets from different participants, and we can temporally split the learning process, detaching the privacy preserving layer from the rest of the learning process to minimize privacy breaches. This paper shows how we can analyze the medical data whilst ensuring privacy using our proposed multi-site spatio-temporal split learning algorithm on Coronavirus Disease-19 (COVID-19) chest Computed Tomography (CT) scans, MUsculoskeletal RAdiographs (MURA) X-ray images, and cholesterol levels.

Recent Advances in Android Mobile Malware Detection: A Systematic Literature Review

In recent years, the global pervasiveness of smartphones has prompted the development of millions of free and commercially available applications. These applications allow users to perform various activities, such as communicating, gaming, and completing financial and educational tasks. These commonly used devices often store sensitive private information and, consequently, have been increasingly targeted by harmful malicious software. This paper focuses on the concepts and risks associated with malware, and reviews current approaches and mechanisms used to detect malware with respect to their methodology, associated datasets, and evaluation metrics.

A New Approach Customizable Distributed Network Service Discovery System

Computer systems and applications on the internet provide services to outsiders and, at the same time, the vulnerabilities may be exploited by attackers and leak some sensitive private information. To collect and monitor the service information provided by the network environment such as IoT (Internet of Things), vehicular networks, cloud computing, and cloud storage, it is particularly important that a system can provide faster service discovery for discovering and identifying specific network services. The current service discovery systems mainly use port scanning technology, including Nmap, Zmap, and Masscan. However, these technologies hard code the service features and only support common services so that cannot cope with real‐time updates and changing network services. To solve the above problems, this paper proposed a customizable distributed network service discovery system based on stateless scanning technology of Masscan and proposed a customizable interactive pattern set syntax. The system used random destination address technologies to scan for Ipv4 address allocation and used a distributed deployment scheme. Experimental results show that the system has high scanning speed and has high adaptability to new services and special services.

A Novel COVID-19 Data Set and an Effective Deep Learning Approach for the De-Identification of Italian Medical Records.

In the last years, the need to de-identify privacy-sensitive information within Electronic Health Records (EHRs) has become increasingly felt and extremely relevant to encourage the sharing and publication of their content in accordance with the restrictions imposed by both national and supranational privacy authorities. In the field of Natural Language Processing (NLP), several deep learning techniques for Named Entity Recognition (NER) have been applied to face this issue, significantly improving the effectiveness in identifying sensitive information in EHRs written in English. However, the lack of data sets in other languages has strongly limited their applicability and performance evaluation. To this aim, a new de-identification data set in Italian has been developed in this work, starting from the 115 COVID-19 EHRs provided by the Italian Society of Radiology (SIRM): 65 were used for training and development, the remaining 50 were used for testing. The data set was labelled following the guidelines of the i2b2 2014 de-identification track. As additional contribution, combined with the best performing Bi-LSTM + CRF sequence labeling architecture, a stacked word representation form, not yet experimented for the Italian clinical de-identification scenario, has been tested, based both on a contextualized linguistic model to manage word polysemy and its morpho-syntactic variations and on sub-word embeddings to better capture latent syntactic and semantic similarities. Finally, other cutting-edge approaches were compared with the proposed model, which achieved the best performance highlighting the goodness of the promoted approach.

Hap-Cp-Abe Based Encryption Technique With Hashed Access Policy Based Authentication Scheme For Privacy Preserving Of Phr

As a novel abridged record of patients' medical data, PHR would be accessed online. Nevertheless, this very sensitive private information might be attained through illegal parties as of the cloud. Numerous existent algorithms utilized CP-ABE to protect the data as of unapproved users, however, it loses the high-efficiency against the conventional methods on account of the security parameters’ requirement of large size. To surmount these issues, a HAP-CP-ABE-based encryption technique with HAP based authentication scheme intended for PHR is proposed. The proposed work encompasses '3′ phases: (A) the authentication phase, which includes verification before the user uploads the PHR files. (B) Secure upload phase: initially, the features are all extracted as of the PHR files. Then, the features are lessened as of the extracted features utilizing ENT-LDA. Then, HAP will be created using SHA-512. Lastly, securely upload PHR to cloud utilizing HAP-CP-ABE. (C) In the secure download phase, primarily, the system corroborates the HAP for the requested user. If it is matched, then it permits them to access, or else the system declined permission. After authentication, the system permits the requested user to download the file. Extensive analysis along with investigational outcomes shows the proposed system's security together with efficiency.

Prognostic factors analysis for oral cavity cancer survival in the Netherlands and Taiwan using a privacy-preserving federated infrastructure

The difference in incidence of oral cavity cancer (OCC) between Taiwan and the Netherlands is striking. Different risk factors and treatment expertise may result in survival differences between the two countries. However due to regulatory restrictions, patient-level analyses of combined data from the Netherlands and Taiwan are infeasible. We implemented a software infrastructure for federated analyses on data from multiple organisations. We included 41,633‬ patients with single-tumour OCC between 2004 and 2016, undergoing surgery, from the Taiwan Cancer Registry and Netherlands Cancer Registry. Federated Cox Proportional Hazard was used to analyse associations between patient and tumour characteristics, country, treatment and hospital volume with survival. Five factors showed differential effects on survival of OCC patients in the Netherlands and Taiwan: age at diagnosis, stage, grade, treatment and hospital volume. The risk of death for OCC patients younger than 60 years, with advanced stage, higher grade or receiving adjuvant therapy after surgery was lower in the Netherlands than in Taiwan; but patients older than 70 years, with early stage, lower grade and receiving surgery alone in the Netherlands were at higher risk of death than those in Taiwan. The mortality risk of OCC in Taiwanese patients treated in hospitals with higher hospital volume (≥ 50 surgeries per year) was lower than in Dutch patients. We conducted analyses without exchanging patient-level information, overcoming barriers for sharing privacy sensitive information. The outcomes of patients treated in the Netherlands and Taiwan were slightly different after controlling for other prognostic factors.

Mobile app privacy in software engineering research: A systematic mapping study

Abstract Context: Mobile applications (apps) have become deeply personal, constantly demanding access to privacy-sensitive information in exchange for more personalized user experiences. Such privacy-invading practices have generated major multidimensional privacy concerns among app users. Objective: The research on mobile app privacy has experienced rapid growth over the past decade. This line of research is aimed at systematically exposing the privacy practices of apps and proposing solutions to protect the privacy of mobile app users. In this paper, we conduct a systematic mapping study of this body of research. Our objectives are to a) explore trends in SE app privacy research, b) categorize existing evidence, and c) identify potential directions for future research. Method: A systematic mapping study of 59 Software Engineering (SE) primary studies on mobile app privacy. Our scope is studies published in software engineering venues between 2008 and 2018. Results: Our results show that existing literature can be divided into four main categories: privacy policy, requirements, user perspective, and leak detection. Furthermore, our survey reveals an imbalance between these categories—the majority of existing research focuses on proposing tools for detecting privacy leaks, with fewer studies targeting privacy requirements and policy and even fewer on user perspective. Conclusions: Our survey exposes several gaps in existing research and suggests areas for improvement.

Blockchain-Based Quality of Service for Healthcare System in the Gaza Strip

Electronic Health Records EHRs are critical, highly sensitive private information in healthcare, and need to be frequently shared among different parties for example patients, physicians and administration. Blockchain provides a shared, immutable and transparent health records of all the transactions to build applications with trust, accountability, and transparency. This provides a unique opportunity to develop a secure and trustable EHR data management and sharing system using blockchain. This study aims to develop the use of health records as well as finding the current status of EHRs by designing a checklist to measure the extent of use of health records in the Gaza Strip hospitals and exploring the possibilities of using blockchain technology to develop the use of the electronic health records to share accurate and complete health data between multiple parties such as patients, doctors, and managers in an effective, transparent and secure manner.

Differentially Private ADMM for Regularized Consensus Optimization

Due to its broad applicability in machine learning, resource allocation, and control, the alternating direction method of multipliers (ADMM) has been extensively studied in the literature. The message exchange of the ADMM in multiagent optimization may reveal sensitive information of agents, which can be overheard by malicious attackers. This drawback hinders the application of the ADMM to privacy-aware multiagent systems. In this article, we consider consensus optimization with regularization, in which the cost function of each agent contains private sensitive information, e.g., private data in machine learning, and private usage patterns in resource allocation. We develop a variant of the ADMM that can preserve agents' differential privacy by injecting noise into the public signals broadcast to the agents. We derive conditions on the magnitudes of the added noise under which the designated level of differential privacy can be achieved. Furthermore, the convergence properties of the proposed differentially private ADMM are analyzed under the assumption that the cost functions are strongly convex with Lipschitz continuous gradients, and the regularizer has smooth gradients or bounded subgradients. We find that to attain the best convergence performance given a certain privacy level, the magnitude of the injected noise should decrease as the algorithm progresses. Additionally, the choice of the number of iterations should balance the tradeoff between the convergence, and the privacy leakage of the ADMM, which is explicitly characterized by the derived upper bounds on convergence performance. Finally, numerical results are presented to corroborate the efficacy of the proposed algorithm. In particular, we apply the proposed algorithm to multiagent linear-quadratic control with private information to showcase its merit in control applications.

A new ‐map mechanism for mobility traces privacy

SummaryA major concern of the deployment of location based services (LBSs) is the safeguards of the user's location data collected by service providers, since personal location data may imply sensitive private information. However, most available works proposed so far rely on syntactic privacy models such as k‐anonymity and location perturbation, which are proved to quiver in the balance with privacy and usability requirements. In this article, we provide a new ‐map mechanism to help users better understand the privacy/accuracy tradeoff process and preserve location data. In our ‐map model, the user can specify a geographic region to hide her precise location and suppress the following queries in the same area to meet her privacy requirement, while maintaining and understanding its usability. In addition, we propose a new notion of ‐privacy based on differential privacy to account for the temporal‐spatial correlation and history correlation in the case of crossing region, which is the major privacy concern of a moving user's trace. Finally, we evaluate our framework by using an online LBS with real‐world data sets. The results not only indicate that the ‐map is significantly useful for identifying the privacy and utility tradeoffs but also show the effectiveness and practicality of the proposed ‐privacy.

Research on E-commerce Payment Security and Privacy Protection Based on Improved B2C Model

The popularity of Internet and mobile terminalspromotes the development of e-commerce. However, e-commercewhich is not necessary to face to face poses a major challenge to thepayment security and privacy protection of consumers. This paperbriefly introduces the traditional business to customer (B2C)e-commerce model. A third-party privacy server was introduced toprovide a double encryption algorithm for the model, and then thetraditional and improved B2C models were simulated to verify theperformance of the two models in the confidentiality of the transactioninformation and the security of the transaction informationtransmission process. The results showed that the intruder could notdirectly obtain the sensitive privacy information such as paymentinformation and order information of consumers even if he invaded thedatabase of the online store when the improved B2C model was used;after the transaction information was intercepted and decrypted underthe two B2C models, the decryption integrity decreased with theincrease of the transaction information quantity, while under the sametransaction information quantity, the decryption integrity of theimproved B2C model was lower

QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search

With the advancement in ICT, web search engines have become a preferred source to find health-related information published over the Internet. Google alone receives more than one billion health-related queries on a daily basis. However, in order to provide the results most relevant to the user, WSEs maintain the users’ profiles. These profiles may contain private and sensitive information such as the user’s health condition, disease status, and others. Health-related queries contain privacy-sensitive information that may infringe user’s privacy, as the identity of a user is exposed and may be misused by the WSE and third parties. This raises serious concerns since the identity of a user is exposed and may be misused by third parties. One well-known solution to preserve privacy involves issuing the queries via peer-to-peer private information retrieval protocol, such as useless user profile (UUP), thereby hiding the user’s identity from the WSE. This paper investigates the level of protection offered by UUP. For this purpose, we present QuPiD (query profile distance) attack: a machine learning-based attack that evaluates the effectiveness of UUP in privacy protection. QuPiD attack determines the distance between the user’s profile (web search history) and upcoming query using our proposed novel feature vector. The experiments were conducted using ten classification algorithms belonging to the tree-based, rule-based, lazy learner, metaheuristic, and Bayesian families for the sake of comparison. Furthermore, two subsets of an America Online dataset (noisy and clean datasets) were used for experimentation. The results show that the proposed QuPiD attack associates more than 70% queries to the correct user with a precision of over 72% for the clean dataset, while for the noisy dataset, the proposed QuPiD attack associates more than 40% queries to the correct user with 70% precision.

Online meta-learning firewall to prevent phishing attacks

Phishing is the most well-known act of deceiving the Internet users, in which the ‘perpetrator’ plays a credible entity. This is done by misusing the inadequate protection provided by electronic tools, and by exploiting the ignorance of the user-object, in order to illegally obtain personal data, such as sensitive private information and passwords. This research proposes the online meta-learning firewall to prevent phishing attacks. It is a highly innovative and fully automated active safety tool that uses a long short-term memory meta-learner algorithm. This method can learn to efficiently classify using a small number of samples. At the same time, it can converge with a fairly small number of steps. The proposed system is an improvement on the k-nearest neighbor with self-adjusting memory algorithm, which is inspired by the model of short and long-term memory. The purpose of the system is to understand the nature of an unknown situation and to classify it, based on the most relevant characteristics that come directly from the unknown environment.

Measurement and control of geo-location privacy on Twitter

The widespread diffusion of Online Social Networks and Media (OSNEM) has generated a huge amount of users’ personal data. As this data is often publicly available, users’ privacy is at risk. To address this issue, users may control the release of their sensitive data on OSNEM. An example of data that users rarely publish is their location. Besides being a privacy-sensitive information, location is a business-relevant data that third parties, e.g., Location-Based Service (LBS) providers, may be interested to obtain. It is, therefore, of paramount importance to understand to what extent the secrecy of location information can be violated. In this work, we investigate how users can measure the privacy of their geo-location on OSNEM and to control the factors affecting it. We define the privacy of a target user as the geographical distance between her actual unexposed location and the location estimated by an attacker. To measure privacy, we propose a novel deep learning architecture that uncovers a target user’s position based only on the publicly-available locations shared by users on Twitter. Results show that locations can be accurately unveiled for the majority of the users, thus suggesting the need for countermeasures to improve their privacy. To control privacy, we propose data perturbation techniques that users can apply to tune the public exposure of their location, and we show the resulting privacy improvements. To shed light on the factors influencing privacy, we then propose a machine learning model that measures privacy based on several users’ features (e.g., social and behavioral characteristics). Unlike the aforementioned deep learning approach, this model also allows to quantify the impact that each feature has on privacy. We observe that features related to the history of users’ visited locations proved to be the most relevant factors affecting privacy. Finally, we explore potential side effects resulting from the application of data perturbation strategies. In particular, we examine, as a study case, the trade-off between users’ privacy and the effectiveness of a proximity marketing LBS. Results suggest that privacy can be guaranteed while not significantly lowering the effectiveness of the LBS.

An Improved Sanitization Algorithm in Privacy-Preserving Utility Mining

High-utility pattern mining is an effective technique that extracts significant information from varied types of databases. However, the analysis of data with sensitive private information may cause privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, privacy-preserving utility mining (PPUM) has become an important research topic in recent years. The MSICF algorithm is a sanitization algorithm for PPUM. It selects the item based on the conflict count and identifies the victim transaction based on the concept of utility. Although MSICF is effective, the heuristic selection strategy can be improved to obtain a lower ratio of side effects. In our paper, we propose an improved sanitization approach named the Improved Maximum Sensitive Itemsets Conflict First Algorithm (IMSICF) to address this issue. It dynamically calculates conflict counts of sensitive items in the sanitization process. In addition, IMSICF chooses the transaction with the minimum number of nonsensitive itemsets and the maximum utility in a sensitive itemset for modification. Extensive experiments have been conducted on various datasets to evaluate the effectiveness of our proposed algorithm. The results show that IMSICF outperforms other state-of-the-art algorithms in terms of minimizing side effects on nonsensitive information. Moreover, the influence of correlation among itemsets on various sanitization algorithms’ performance is observed.