Providing Privacy-Aware Incentives in Mobile Sensing Systems

Multifunctional adversarial examples: A novel mechanism for authenticatable privacy protection of images

The security issues of e-Commerce are related to economic loss and privacy leak. The all kind of fraud and crime are mainly due to the anonymity in the world of Internet. These issues can efficiently be controlled by managing the true identity of e-Commerce transaction partners. The true identity of the transaction partners can be used to track and locate the unlawful activities performed by a person. The vulnerability issues in the e-Commerce linked with anonymity can also be handled by the true identity mechanism. The mechanism will not only be suitable for the e-Commerce but also can be used for the overall network security breached by the illegal perpetrator taking the advantage of anonymity on the Internet. The anonymity is one of the strengths of e-Commerce. Therefore, revealing the identity is a loss in the generic strength of e-Commerce. In this paper, I have proposed a mechanism to manage the true identity of the transaction partners without the loss of anonymity with the help of a trusted third party. The mechanism is based upon cryptographic technique and a trusted third party identity distribution center. The mechanism has strong capability of fraud control over the Internet, taking place due to anonymity.

The widespread use of location-based services (LBS), in which any informative service is provided simply based on the user's present location, has generated serious concerns about the user's location privacy. For example, if a customer wants to know “where is the nearest ATM machine?”, she must provide her exact geographical coordinates in order to receive the location-based informative services she requested. Although location-based services open up a wide range of markets and give remarkable convenience to the end user, they also bring minor privacy threats to the user's location data. The requirement that the user informs the LBS provider of their current location in order to get connected services exposes the system to a risk of privacy invasion. Because the volume of data collected from moving or stationary mobile users utilizing LBS may be large, it is critical to design safe frameworks and systems that keep location information private. The two most common techniques to preserving location privacy in LBS are cloaking and obfuscation. These solutions leverage a trusted third party (TTP) and the K-anonymity principle to make the query issuer indistinguishable from other K 1 additional users. The thesis's initial goal is to investigate the user privacy concerns raised by location-based services, and it presents the VIC-PRO scheme, a TTP-based technique to avoid location-based proximity inference of the user who submits a query. The proposed VIC-PRO technique enhances the privacy of query initiating proximity information and helps to close the holes in the present system's K-anonymity approach. In TTP-based methods, all data (namely location coordinates and query) is available at the central server; hence, the central node has complete awareness of the query (including user ID). This is a fundamental flaw in TTP-based design, and it renders these frameworks vulnerable to various privacy assaults. We observe the need for collaboration-based communication between peer users who belong to the population of mobile users in a decentralized or TTP-free architecture and propose CAST (acronym for CAching with truST), a collaborative P2P communication model employing a cascading series of trust between peers, where peers use cached data to collaborate with one another and the results can be obtained. The method operates effectively and gives results locally with little latency when the peers have similar interests (or data value). Under a pull-based sporadic query situation, the suggested algorithm prioritizes user privacy and performs well. We utilize the benefits and mitigate the flaws of both traditional current techniques since measuring and evaluating peer trust have always been a vital aspect. We offer HYB, a hybrid approach, for achieving location privacy for mobile users that regularly utilize location services. The proposed HYB method is based on collaborative location data preprocessing and makes use of homomorphic encryption techniques. Location privacy may be accomplished on two levels: close proximity and far distance. Under a particular, pull-based, occasional query situation, the suggested privacy algorithms successfully safeguard user location privacy. The privacy issue becomes critical when location-tagged data publications, such as public healthcare data and regional criminal history data, are constructed in reverse order by a challenger to locate the authentic consumer against the location specified in the specific record tuple. In most circumstances, address information is regarded as one of the most sensitive aspects of the public record. Any association of such data with a publicly released identifier that has quasi-property has the potential to reveal a great regarding a user (that would normally remain concealed) or, in the worst-case scenario, ruin the user's social reputation. We determine the current situation in geospatial masking, undertake a thorough study of current masking approaches, as well as design three-layer iterative RDV masking, a viable technique that gives location secrecy without rendering publicly available data useless. The recommended approach is excellent for geo-referenced, consistent, granular point published data.

The sensing and communication capabilities of the Internet of Vehicles (IoV) allow for the design of safer Intelligent Transportation Systems (ITS), with capabilities for reduced crashes and drivers' injuries. To this aim, entities in the IoV can autonomously exchange Warning Messages (WMs) on the road status to help drivers, passengers, and vehicles to take immediate actions and reduce road accidents. WMs authentication, timely propagation, and integrity is a fundamental requirement, as the injection of false WMs or their delay by a malicious user can lead to accidents or traffic congestion. The currently available WMs authentication and propagation schemes focus on the security properties, without however accounting for the scalability issues related to the large number of entities connected in the IoV. In this paper, we propose Secure Warning Message Authentication and Propagation (SWAP), an authentication protocol specifically designed for WM propagation in the Internet of Vehicles (IoV). We introduce the Warning Message Authority (WMA), a novel entity responsible for secure WMs handling. Thanks to the WMA, WMs are managed by dedicated resources that allow for the timely delivery of safety-critical information, avoiding delays due to network congestion at the Trusted Authority (TA). We prove the security of SWAP by using the Random Oracle Model (ROM) and via security analysis. Compared to state of the art authentication schemes, SWAP reduces the number of communication bits by 100 bits, and provides a ten-fold decrease of the computation time.

The privacy leakage resulting from location-based service (LBS) has become a critical issue. To preserve user privacy, many previous studies have investigated to prevent LBS servers from user privacy theft. However, they only consider whether the peers are innocent or malicious but ignore the relationship between the peers, whereas such a relationship between each pairwise of users affects the privacy leakage tremendously. For instance, a user has less concern of privacy leakage from a social friend than a stranger. In this paper, we study cyber-physical-social (CPS) aware method to address the privacy preserving in the case that not only LBS servers but also every other participant in the network has the probability to be malicious. Furthermore, by exploring the physical coupling and social ties among users, we construct CPS-aware privacy utility maximization (CPUM) game. We then study the potential Nash equilibrium of the game and show the existence of Nash equilibrium of CPUM game. Finally, we design a CPS-aware algorithm to find the Nash equilibrium for the maximization of privacy utility. Extensive evaluation results show that the proposed approach reduces privacy leakage by 50% in the case that malicious servers and users exist in the network.

With the continuous development of unmanned aerial vehicle (UAV) photography and mapping technology, there are more and more air-to-ground information acquisition methods, which provide more choices for both industry and entertainment. Under the help of drones, operators can obtain and collect information on the ground, and air-to-ground images are often the convenient types. However, the issue of privacy protection can not be ignored here. In special situations, it will violate the right of privacy. Faced with such a problem, how to properly prevent privacy leakage and protect air-to-ground information security has become an unavoidable topic. In this paper, we make a detailed survey to summarize the relevant both technical and legal issues. Specially, the whole survey can be divided into four main parts: overview of airborne optical equipment, possible privacy infringement methods based on drones, privacy protection in drones applications and related legal aspects. Based on the survey content in this article, we can set up several valuable research directions in this field and improve the privacy protection in civilian drones applications. More effective privacy protection methods will also be applied to industry to enhance the application effect of UAV aerial photography technology.

The widespread usage of location based services (LBS); where obtaining any informational service is entirely based upon the user's current location, have raised a significant concern about location privacy of the user. For the queries like ‘where is my closest ATM?’, ‘where is my nearest hospital’, or any route assistance in general, it is essential to submit the user's actual location to avail the demanded services. Similar communication holds true for a location based vehicular transportation system. Cloaking and ob-fuscation are the two generalized approaches to deal with location privacy preservation in LBS. These approaches are mainly based on a trusted third party (TTP) and exploits the well established K-anonymity principle in order to make the query issuer indistinguishable with other K − 1 more users. In such approaches all the data (mainly location coordinates and queries) becomes available at the central server, thus complete knowledge of the query (including user id) exists at central node. This is the major limitation of TTP based architecture and makes such frameworks susceptible to different privacy attacks. This paper is a research attempt to extend the realm of collaborative communication among peers belonging to a mobile user group in a decentralized or trusted third party free architecture. We propose a collaborative P2P communication model; called CAST, that employs the series of trust among peers and peers use their cached mobile data to collaborate with each other in order to get the results locally. The scheme provides results locally with low latency and works efficiently when the peers share common inclinations (or data value). The proposed algorithm preserves user's privacy and performs effectively under pull-based sporadic query scenario.

Blind signature allows a user to obtain a signature from an authority on any document, in such a way that the authority learns nothing about the message that is being signed. The blindness is an important property which distinguishes the blind signature from other signature schemes. Partially blind signature is an extension of blind signature that allows a signer to sign a partially blinded message that include pre-agreed information in the resulting signatures. Recently, Liao et al. proposed a pairing-based provable blind signature scheme without random oracles, and Hu et al. proposed an efficient ID-based partially blind signature scheme with security proof. But in this paper, we show that the two schemes do not satisfy the blindness and partially blindness, respectively. Liao et al.'s scheme is linkability and in Hu et al.'s scheme, a malicious user can replace pre-agreed common information with a special information

Reimagining Patient Data Access for Researchers

Based on the statistics, advertisements (ads) generate more than 80% of companies' revenues. However, the complexity of the ads ecosystem blurs the boundaries of responsibility between companies. It can not analyze privacy and security issues in such an ecosystem. For example, collecting user information without user consent for data analysis and displaying ads will cause privacy leakage. But we can not explain which types of the company need to take measures to protect privacy. In our paper, to clarify the responsibility of companies in the advertisement ecosystem, we divide them into six types of entities according to their needs and functions. Then, we design a lightweight simulation platform to illustrate the advertisement ecosystem and support security and privacy analysis. Finally, we take personal ads recommendations based on federated learning as an example to verify the feasibility for privacy and security analysis in this platform.

The increasing presence of renewable sources requires power grid operators to continuously monitor electricity generation and demand in order to maintain the grid's stability. To this end, smart meters have been deployed to collect realtime information about the current grid load and forward it to the utility in a timely manner. High resolution smart meter data can however reveal the nature of appliances and their mode of operation with high accuracy, and thus endanger user privacy. In this paper, we investigate the impact on user privacy when the consumption data collected by distributed smart metering devices are preprocessed prior to their usage. We therefore assess the impact on the successful classification of appliances when sensor readings are (1) quantized, (2) down-sampled at a lower sampling rate, and (3) averaged by means of an FIR filter. Our evaluation shows that a combination of these preprocessing steps can provide a balanced trade-off that is in the interests of both users (privacy protection) and utilities (near real-time information).

The continued growth of Social Networking Services (SNS) has significant implications for computer and cybercrime management. In exploring the human side of security, studies have shown that any security response requires more than technical solutions. This is even more so as users are often seen as the main point of vulnerabilities for computer systems including SNS. This study seeks to probe the important values in understanding how to maximize end-user security and privacy concerns in SNS. The study applies the Value-focused Thinking methodology to uncover the SNS users’ values in the form of their fundamental objectives/wishes, with an initial focus on developing economies in the Caribbean. The study found that privacy and integrity of SNS, security controls, corporate social responsibility and personal responsibility are fundamental in maximizing user security and privacy.

With the rise of digital images in our daily lives, there is a growing need to provide an image trading market where people can monetize their images and get desired images at prices that fit their budget. Those images are usually uploaded and stored onto centralized image trading service providers’ servers and the transactions for image trading are processed by these providers. Unfortunately, transaction unfairness and users’ privacy breaches have become major concerns since the service providers might be untrusted and able to manipulate image trading prices and infer users’ private information. Recently, several approaches have been proposed to address the unfairness issue by using the decentralized ledger technique and smart contract, but users’ privacy protection is not considered. In this paper, we propose a fair and privacy-preserving protocol that supports image fair exchange and protect user privacy. In particular, we exploit blockchain and Merkle tree to construct a fair image trading protocol with low communication overhead based on smart contract, which serves as an external judge that resolves disputes between buyers and sellers in image transactions. Moreover, we extend a popular short group signature scheme to protect users’ identity privacy, prevent linkability of transactions from being inferred, and ensure traceability of malicious users who may sell fake images and/or refuse to pay. Finally, we design and build a practical and open-source image trading system to evaluate the performance of our proposed protocol. Experimental results demonstrate its effectiveness and efficiency in real-world applications.

In the future 5G paradigm, billions of machinetype devices will be deployed to enable wide-area and ubiquitous data sensing, collection, and transmission. Considering the traffic characteristics of machine-to-machine (M2M) communications and the spectrum shortage dilemma, a cost-efficient solution is to share the underutilized spectrum allocated to human-to-human (H2H) users with M2M devices in an opportunistic manner. However, the implementation of large-scale spectrum sharing in 5G heterogeneous networks confronts many challenges, including lack of incentive mechanism, privacy leakage, security threats, and so on. This motivates us to develop a privacy-preserved, incentive-compatible, and spectrum-efficient framework based on blockchain, which is implemented in two stages. First, H2H users sign a contract with the base station for spectrum sharing, and receive dedicated payments based on their contributions. Next, the shared spectrum is allocated to M2M devices to maximize the total throughput. We elaborate the operation details of secure spectrum sharing, incentive mechanism design, and efficient spectrum allocation. A case study is presented to demonstrate the security and efficiency of the proposed framework. Finally, we outline several open issues and conclude this article.

FedCDR: Privacy-preserving federated cross-domain recommendation