scholarly journals Privacy Threats of Acoustic Covert Communication among Smart Mobile Devices

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Li Duan ◽  
Kejia Zhang ◽  
Bo Cheng ◽  
Bingfei Ren
Keyword(s):  
Private Information ◽  
High Frequency ◽  
Masking Effect ◽  
Smart Devices ◽  
Covert Communication ◽  
Auditory Masking ◽  
User Privacy ◽  
Privacy Threats ◽  
Communication Technique ◽  
Smart Mobile

The emerging, overclocking signal-based acoustic covert communication technique allows smart devices to communicate (without users’ consent) utilizing their microphones and speakers in ultrasonic side channels, which offers users imperceptible and convenient personalized services, e.g., cross-device authentication and media tracking. However, microphones and speakers could be maliciously used and pose severe privacy threats to users. In this paper, we propose a novel high-frequency filtering- (HFF-) based protection model, named UltraFilter, which protects user privacy by enabling users to selectively filter out high-frequency signals from the metadata received by the device. We also analyze the feasibility of using audio frequencies (i.e., ≤18 kHz) to the acoustic covert communication and carry out the acoustic covert communication system by introducing the auditory masking effect. Experiments show that UltraFilter can prevent users’ private information from leaking and reduce system load and that the audio frequencies can pose threats to user privacy.

scholarly journals Privacy in targeted advertising: A survey

2020 ◽  
Author(s):  
Imdad Ullah ◽  
Roksana Boreli ◽  
Salil S. Kanhere
Keyword(s):  
Private Information ◽  
Personal Data ◽  
Third Party ◽  
User Privacy ◽  
Research Issues ◽  
Privacy Concerns ◽  
Targeted Advertising ◽  
Privacy Threats ◽  
Measurement Analysis ◽  
Privacy Risks

Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among individuals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues.<br>

scholarly journals Privacy in targeted advertising: A survey

2020 ◽  
Author(s):  
Imdad Ullah ◽  
Roksana Boreli ◽  
Salil S. Kanhere
Keyword(s):  
Private Information ◽  
Personal Data ◽  
Third Party ◽  
User Privacy ◽  
Research Issues ◽  
Privacy Concerns ◽  
Targeted Advertising ◽  
Privacy Threats ◽  
Measurement Analysis ◽  
Privacy Risks

Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among individuals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues.<br>

scholarly journals Privacy in targeted advertising: A survey

2020 ◽  
Author(s):  
Imdad Ullah ◽  
Roksana Boreli ◽  
Salil S. Kanhere
Keyword(s):  
Private Information ◽  
Personal Data ◽  
Third Party ◽  
User Privacy ◽  
Research Issues ◽  
Privacy Concerns ◽  
Targeted Advertising ◽  
Privacy Threats ◽  
Measurement Analysis ◽  
Privacy Risks

Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among individuals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues.<br>

scholarly journals Information Privacy Concerns Among Instagram Users: The Case of Indonesian College Students

2020 ◽  
Vol 6 (2) ◽  
pp. 159
Author(s):  
Eko Wahyu Tyas Darmaningrat ◽  
Hanim Maria Astuti ◽  
Fadhila Alfi
Keyword(s):  
College Students ◽  
Private Information ◽  
Structural Equation ◽  
Personal Information ◽  
Information Privacy ◽  
Risk Awareness ◽  
User Privacy ◽  
Privacy Concerns ◽  
Information Privacy Concerns ◽  
Increased Risk

Background: Teenagers in Indonesia have an open nature and satisfy their desire to exist by uploading photos or videos and writing posts on Instagram. The habit of uploading photos, videos, or writings containing their personal information can be dangerous and potentially cause user privacy problems. Several criminal cases caused by information misuse have occurred in Indonesia.Objective: This paper investigates information privacy concerns among Instagram users in Indonesia, more specifically amongst college students, the largest user group of Instagram in Indonesia.Methods: This study referred to the Internet Users' Information Privacy Concerns (IUIPC) method by collecting data through the distribution of online questionnaires and analyzed the data by using Structural Equation Modelling (SEM).Results: The research finding showed that even though students are mindful of the potential danger of information misuse in Instagram, it does not affect their intention to use Instagram. Other factors that influence Indonesian college students' trust are Instagram's reputation, the number of users who use Instagram, the ease of using Instagram, the skills and knowledge of Indonesian students about Instagram, and the privacy settings that Instagram has.Conclusion: The awareness and concern of Indonesian college students for information privacy will significantly influence the increased risk awareness of information privacy. However, the increase in risk awareness does not directly affect Indonesian college students' behavior to post their private information on Instagram.

scholarly journals A Novel High-Capacity Behavioral Steganographic Method Combining Timestamp Modulation and Carrier Selection Based on Social Networks

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 111
Author(s):  
Mingliang Zhang ◽  
Zhenyu Li ◽  
Pei Zhang ◽  
Yi Zhang ◽  
Xiangyang Luo
Keyword(s):  
Social Networks ◽  
High Frequency ◽  
High Capacity ◽  
Extraction Process ◽  
Secret Message ◽  
Covert Communication ◽  
Embedding Capacity ◽  
Carrier Selection ◽  
Text Steganography

Behavioral steganography is a method used to achieve covert communication based on the sender’s behaviors. It has attracted a great deal of attention due to its robustness and wide application scenarios. Current behavioral steganographic methods are still difficult to apply in practice because of their limited embedding capacity. To this end, this paper proposes a novel high-capacity behavioral steganographic method combining timestamp modulation and carrier selection based on social networks. It is a steganographic method where the embedding process and the extraction process are symmetric. When sending a secret message, the method first maps the secret message to a set of high-frequency keywords and divides them into keyword subsets. Then, the posts containing the keyword subsets are retrieved on social networks. Next, the positions of the keywords in the posts are modulated as the timestamps. Finally, the stego behaviors applied to the retrieved posts are generated. This method does not modify the content of the carrier, which ensures the naturalness of the posts. Compared with typical behavioral steganographic methods, the embedding capacity of the proposed method is 29.23∼51.47 times higher than that of others. Compared to generative text steganography, the embedding capacity is improved by 16.26∼23.94%.

Toward Responsible AI: An Overview of Federated Learning for User-centered Privacy-preserving Computing

2021 ◽  
Vol 11 (3-4) ◽  
pp. 1-22
Author(s):  
Qiang Yang
Keyword(s):  
Private Information ◽  
High Performance ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Brute Force ◽  
New Paradigm ◽  
User Privacy ◽  
Data Governance ◽  
Incentive Mechanisms ◽  
Rule Out

With the rapid advances of Artificial Intelligence (AI) technologies and applications, an increasing concern is on the development and application of responsible AI technologies. Building AI technologies or machine-learning models often requires massive amounts of data, which may include sensitive, user private information to be collected from different sites or countries. Privacy, security, and data governance constraints rule out a brute force process in the acquisition and integration of these data. It is thus a serious challenge to protect user privacy while achieving high-performance models. This article reviews recent progress of federated learning in addressing this challenge in the context of privacy-preserving computing. Federated learning allows global AI models to be trained and used among multiple decentralized data sources with high security and privacy guarantees, as well as sound incentive mechanisms. This article presents the background, motivations, definitions, architectures, and applications of federated learning as a new paradigm for building privacy-preserving, responsible AI ecosystems.

Privacy Information Leakage Prevention in Cognitive Social Mining Applications

2019 ◽  
pp. 188-212
Author(s):  
Suriya Murugan ◽  
Anandakumar H.
Keyword(s):  
Social Networks ◽  
Social Relations ◽  
Private Information ◽  
Online Social Networks ◽  
Information Leakage ◽  
User Profile ◽  
User Privacy ◽  
Sensitive Data ◽  
Data Set ◽  
Secure Information

Online social networks, such as Facebook are increasingly used by many users and these networks allow people to publish and share their data to their friends. The problem is user privacy information can be inferred via social relations. This chapter makes a study and performs research on managing those confidential information leakages which is a challenging issue in social networks. It is possible to use learning methods on user released data to predict private information. Since the main goal is to distribute social network data while preventing sensitive data disclosure, it can be achieved through sanitization techniques. Then the effectiveness of those techniques is explored, and the methods of collective inference are used to discover sensitive attributes of the user profile data set. Hence, sanitization methods can be used efficiently to decrease the accuracy of both local and relational classifiers and allow secure information sharing by maintaining user privacy.

Emergency Response and Post-Disaster Recovery Using Smartphone-Based Applications

2021 ◽  
pp. 31-49
Author(s):  
Shahriar Kaisar
Keyword(s):  
Natural Disasters ◽  
Ad Hoc ◽  
Communication Technologies ◽  
Relevant Information ◽  
Smart Devices ◽  
Practical Implementation ◽  
Rescue Workers ◽  
Communication Links ◽  
Internet Connections ◽  
Smart Mobile

The number of natural disasters, such as tsunamis, earthquakes, flooding, cyclone, and bushfires, is rapidly increasing globally, and they are claiming thousands of lives while destroying numerous properties. One of the major concerns of these natural disasters is the destruction of communication links, such as powerline and Internet connections, which make it difficult to enable communication among the affected people and the rescue teams. However, the evolution of smart devices equipped with multiple short-range communication technologies, such as Bluetooth and Wi-Fi provides an opportunity to form an ad-hoc network with co-located smart mobile device users and communicate their positions and other relevant information to the rescue workers. This chapter provides a detailed description of recent advancement in this area and highlights important aspects that are needed to be considered for practical implementation.

scholarly journals Privacy-Protection Scheme Based on Sanitizable Signature for Smart Mobile Medical Scenarios

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zhiyan Xu ◽  
Min Luo ◽  
Neeraj Kumar ◽  
Pandi Vijayakumar ◽  
Li Li
Keyword(s):  
Data Security ◽  
Privacy Protection ◽  
Medical Data ◽  
Security And Privacy ◽  
Smart Devices ◽  
Protection Scheme ◽  
Mobile Medicine ◽  
Time Space ◽  
Medical Network ◽  
Smart Mobile

With the popularization of wireless communication and smart devices in the medical field, mobile medicine has attracted more and more attention because it can break through the limitations of time, space, and objects and provide more efficient and quality medical services. However, the characteristics of a mobile smart medical network make it more susceptible to security threats such as data integrity damage and privacy leakage than those of traditional wired networks. In recent years, many digital signature schemes have been proposed to alleviate some of these challenges. Unfortunately, traditional digital signatures cannot meet the diversity and privacy requirements of medical data applications. In response to this problem, this paper uses the unique security attributes of sanitizable signatures to carry out research on the security and privacy protection of medical data and proposes a data security and privacy protection scheme suitable for smart mobile medical scenarios. Security analysis and performance evaluation show that our new scheme effectively guarantees data security and user privacy while greatly reducing computation and communication costs, making it especially suitable for mobile smart medical application scenarios.

An Improved Spectral Subtraction Algorithm Study Based on Voice Human-Computer Interaction in Cockpit

2010 ◽  
Vol 139-141 ◽  
pp. 2154-2157
Author(s):  
Ji Xiang Lu ◽  
Ping Wang ◽  
Long Yi
Keyword(s):  
Speech Recognition ◽  
Human Computer Interaction ◽  
Speech Enhancement ◽  
Spectral Subtraction ◽  
Masking Effect ◽  
Auditory Masking ◽  
Speech Output ◽  
Voice Interaction ◽  
Speech Information ◽  
The Voice

The voice interaction in cockpit mainly includes speech recognition, enhancement and synthesis. This interaction transfers the speech information to the corresponding orders to make machines in cockpit work unmistaken, also feedback the execution results to users by speech output devices or some other ways. The speech enhancement technology is studied in this paper, aiming at the Voice Interactive. We propose an improved spectral subtraction (SS) algorithm based on auditory masking effect, by using two steps SS. The simulated results based on the segment SNR compared to the traditional SS show the effectiveness and superiority of the improved algorithm.

Sign in / Sign up

Export Citation Format

Share Document