Biblio

Consent is a key measure for privacy protection and needs to be `meaningful' to give people informational power. It is increasingly important that individuals are provided with real choices and are empowered to negotiate for meaningful consent. Meaningful consent is an important area for consideration in IoT systems since privacy is a significant factor impacting on adoption of IoT. Obtaining meaningful consent is becoming increasingly challenging in IoT environments. It is proposed that an ``apparency, pragmatic/semantic transparency model'' adopted for data management could make consent more meaningful, that is, visible, controllable and understandable. The model has illustrated the why and what issues regarding data management for potential meaningful consent [1]. In this paper, we focus on the `how' issue, i.e. how to implement the model in IoT systems. We discuss apparency by focusing on the interactions and data actions in the IoT system; pragmatic transparency by centring on the privacy risks, threats of data actions; and semantic transparency by focusing on the terms and language used by individuals and the experts. We believe that our discussion would elicit more research on the apparency model' in IoT for meaningful consent.

Nowadays, data has become more important as the core resource for the information society. However, with the development of data analysis techniques, the privacy violation such as leakage of sensitive data and personal identification exposure are also increasing. Differential privacy is the technique to satisfy the requirement that any additional information should not be disclosed except information from the database itself. It is well known for protecting the privacy from arbitrary attack. However, recent research argues that there is a several ways to infer sensitive information from data although the differential privacy is applied. One of this inference method is to use the correlation between the data. In this paper, we investigate the new privacy threats using attribute correlation which are not covered by traditional studies and propose a privacy preserving technique that configures the differential privacy's noise parameter to solve this new threat. In the experiment, we show the weaknesses of traditional differential privacy method and validate that the proposed noise parameter configuration method provide a sufficient privacy protection and maintain an accuracy of data utility.

To enhance privacy protection and improve data availability, a differential privacy data protection method ICMD-DP is proposed. Based on insensitive clustering algorithm, ICMD-DP performs differential privacy on the results of ICMD (insensitive clustering method for mixed data). The combination of clustering and differential privacy realizes the differentiation of query sensitivity from single record to group record. At the meanwhile, it reduces the risk of information loss and information disclosure. In addition, to satisfy the requirement of maintaining differential privacy for mixed data, ICMD-DP uses different methods to calculate the distance and centroid of categorical and numerical attributes. Finally, experiments are given to illustrate the availability of the method.

Nowadays, Vehicular ad hoc Network as a special class of Mobile ad hoc Network(MANET), provides plenty of services. However, it also brings the privacy protection issues, and there are conflicts between the privacy protection and the services. In this paper, we will propose a privacy protection algorithm based on group signature including two parts, group signature based anonymous verification and batch verification. The anonymous verification is based on the network model we proposed, which can reduce the trust authority burden by dividing the roadside units into different levels, and the batch verification can reduce the time of message verification in one group. We also prove our algorithm can satisfy the demand of privacy protection. Finally, the simulation shows that the algorithm we proposed is better than the BBS on the length of the signature, time delay and packet loss rate.

Channel state information (CSI) has been recently shown to be useful in performing security attacks in public WiFi environments. By analyzing how CSI is affected by the finger motions, CSI-based attacks can effectively reconstruct text-based passwords and locking patterns. This paper presents WiGuard, a novel system to protect sensitive on-screen gestures in a public place. Our approach carefully exploits the WiFi channel interference to introduce noise into the attacker's CSI measurement to reduce the success rate of the attack. Our approach automatically detects when a CSI-based attack happens. We evaluate our approach by applying it to protect text-based passwords and pattern locks on mobile devices. Experimental results show that our approach is able to reduce the success rate of CSI attacks from 92% to 42% for text-based passwords and from 82% to 22% for pattern lock.

The ubiquity of the Internet and email, have provided a mostly insecure communication medium for the consumer. During the last few decades, we have seen the development of several ways to secure email messages. However, these solutions are inflexible and difficult to use for encrypting email messages to protect security and privacy while communicating or collaborating via email. Under the current paradigm, the arduous process of setting up email encryption is non-intuitive for the average user. The complexity of the current practices has also yielded to incorrect developers' interpretation of architecture which has resulted in interoperability issues. As a result, the lack of simple and easy-to-use infrastructure in current practices means that the consumers still use plain text emails over insecure networks. In this paper, we introduce and describe a novel, holistic model with new techniques for protecting email messages. The architecture of our innovative model is simpler and easier to use than those currently employed. We use the simplified trust model, which can relieve users from having to perform many complex steps to achieve email security. Utilizing the new techniques presented in this paper can safeguard users' email from unauthorized access and protect their privacy. In addition, a simplified infrastructure enables developers to understand the architecture more readily eliminating interoperability.

The Radio Frequency Identification (RFID), as one of the key technologies in sensing layer of the Internet of Things (IoT) framework, has increasingly been deployed in a wide variety of application domains. But the reliability of RFID is still a great concern. This article introduces the group management of RFID passwords method, come up with by YUICHI KOBAYASHI and other researchers, which aimed to reduce the risk of privacy disclosure. But for reason that the password and pass key in the method, which are set to protect the ID, doesn't change and the ID is transmitted directly in the unsafe channel, it causes serious vulnerabilities that may be used by resourceful adversary. Thus, we proposed an improved method by using the random number to encrypt the password and switching the password into the temporally valid information. Besides, the protocol encrypts the ID during to avoid the direct transmission situation significantly increases the reliability.

With the amount of user-contributed image data increasing, it is a potential threat for users that everyone may have the access to gain privacy information. To reduce the possibility of the loss of real information, this paper combines homomorphic encryption scheme and image feature extraction to provide a guarantee for users' privacy. In this paper, the whole system model mainly consists of three parts, including social network service providers (SP), the Interested party (IP) and the applications. Except for the image preprocessing phase, the main operations of feature extraction are conducted in ciphertext domain, which means only SP has the access to the privacy of the users. The extraction algorithm is used to obtain a multi-dimensional histogram descriptor as image feature for each image. As a result, the histogram descriptor can be extracted correctly in encrypted domain in an acceptable time. Besides, the extracted feature can represent the image effectively because of relatively high accuracy. Additionally, many different applications can be conducted by using the encrypted features because of the support of our encryption scheme.

Analytics in big data is maturing and moving towards mass adoption. The emergence of analytics increases the need for innovative tools and methodologies to protect data against privacy violation. Many data anonymization methods were proposed to provide some degree of privacy protection by applying data suppression and other distortion techniques. However, currently available methods suffer from poor scalability, performance and lack of framework standardization. Current anonymization methods are unable to cope with the massive size of data processing. Some of these methods were especially proposed for MapReduce framework to operate in Big Data. However, they still operate in conventional data management approaches. Therefore, there were no remarkable gains in the performance. We introduce a framework that can operate in MapReduce environment to benefit from its advantages, as well as from those in Hadoop ecosystems. Our framework provides a granular user's access that can be tuned to different authorization levels. The proposed solution provides a fine-grained alteration based on the user's authorization level to access MapReduce domain for analytics. Using well-developed role-based access control approaches, this framework is capable of assigning roles to users and map them to relevant data attributes.

Deep web, a hidden and encrypted network that crawls beneath the surface web today has become a social hub for various criminals who carry out their crime through the cyber space and all the crime is being conducted and hosted on the Deep Web. This research paper is an effort to bring forth various techniques and ways in which an internet user can be safe online and protect his privacy through anonymity. Understanding how user's data and private information is phished and what are the risks of sharing personal information on social media.

Because the authentication method based username-password has the disadvantage of easy disclosure and low reliability, and also the excess password management degrades the user experience tremendously, the user is eager to get rid of the bond of the password in order to seek a new way of authentication. Therefore, the multifactor biometrics-based user authentication wins the favor of people with advantages of simplicity, convenience and high reliability, especially in the mobile payment environment. Unfortunately, in the existing scheme, biometric information is stored on the server side. As thus, once the server is hacked by attackers to cause the leakage of the fingerprint information, it will take a deadly threat to the user privacy. Aim at the security problem due to the fingerprint information in the mobile payment environment, we propose a novel multifactor two-server authentication scheme under mobile computing (MTSAS). In the MTSAS, it divides the authentication method and authentication means, in the meanwhile, the user's biometric characteristics cannot leave the user device. And also, MTSAS chooses the different authentication factors depending on the privacy level of the authentication, and then provides the authentication based on the different security levels. BAN logic's result proves that MTSAS has achieved the purpose of authentication, and meets the security requirements. In comparison with other schemes, the analysis shows that the proposed scheme MTSAS not only has the reasonable computational efficiency, but also keeps the superior communication cost.

We present OpenFace, our new open-source face recognition system that approaches state-of-the-art accuracy. Integrating OpenFace with inter-frame tracking, we build RTFace, a mechanism for denaturing video streams that selectively blurs faces according to specified policies at full frame rates. This enables privacy management for live video analytics while providing a secure approach for handling retrospective policy exceptions. Finally, we present a scalable, privacy-aware architecture for large camera networks using RTFace.

The increasing complexity and ubiquity in user connectivity, computing environments, information content, and software, mobile, and web applications transfers the responsibility of privacy management to the individuals. Hence, making it extremely difficult for users to maintain the intelligent and targeted level of privacy protection that they need and desire, while simultaneously maintaining their ability to optimally function. Thus, there is a critical need to develop intelligent, automated, and adaptable privacy management systems that can assist users in managing and protecting their sensitive data in the increasingly complex situations and environments that they find themselves in. This work is a first step in exploring the development of such a system, specifically how user personality traits and other characteristics can be used to help automate determination of user sharing preferences for a variety of user data and situations. The Big-Five personality traits of openness, conscientiousness, extroversion, agreeableness, and neuroticism are examined and used as inputs into several popular machine learning algorithms in order to assess their ability to elicit and predict user privacy preferences. Our results show that the Big-Five personality traits can be used to significantly improve the prediction of user privacy preferences in a number of contexts and situations, and so using machine learning approaches to automate the setting of user privacy preferences has the potential to greatly reduce the burden on users while simultaneously improving the accuracy of their privacy preferences and security.

With the development of modern logistics industry railway freight enterprises as the main traditional logistics enterprises, the service mode is facing many problems. In the era of big data, for railway freight enterprises, coordinated development and sharing of information resources have become the requirements of the times, while how to protect the privacy of citizens has become one of the focus issues of the public. To prevent the disclosure or abuse of the citizens' privacy information, the citizens' privacy needs to be preserved in the process of information opening and sharing. However, most of the existing privacy preserving models cannot to be used to resist attacks with continuously growing background knowledge. This paper presents the method of applying differential privacy to protect associated data, which can be shared in railway freight service association information. First, the original service data need to slice by optimal shard length, then differential method and apriori algorithm is used to add Laplace noise in the Candidate sets. Thus the citizen's privacy information can be protected even if the attacker gets strong background knowledge. Last, sharing associated data to railway information resource partners. The steps and usefulness of the discussed privacy preservation method is illustrated by an example.

As the amount of spatial data gets bigger, organizations realized that it is cheaper and more flexible to keep their data on the Cloud rather than to establish and maintain in-house huge data centers. Though this saves a lot for IT costs, organizations are still concerned about the privacy and security of their data. Encrypting the whole database before uploading it to the Cloud solves the security issue. But querying the database requires downloading and decrypting the data set, which is impractical. In this paper, we propose a new scheme for protecting the privacy and integrity of spatial data stored in the Cloud while being able to execute range queries efficiently. The proposed technique suggests a new index structure to support answering range query over encrypted data set. The proposed indexing scheme is based on the Z-curve. The paper describes a distributed algorithm for answering range queries over spatial data stored on the Cloud. We carried many simulation experiments to measure the performance of the proposed scheme. The experimental results show that the proposed scheme outperforms the most recent schemes by Kim et al. in terms of data redundancy.

Compared to other remote attestation methods, the binary-based approach is the most direct and complete one, but privacy protection has become an important problem. In this paper, we presented an Extended Hash Algorithm (EHA) for privacy protection based on remote attestation method. Based on the traditional Merkle Hash Tree, EHA altered the algorithm of node connection. The new algorithm could ensure the same result in any measure order. The security key is added when the node connection calculation is performed, which ensures the security of the value calculated by the Merkle node. By the final analysis, we can see that the remote attestation using EHA has better privacy protection and execution performance compared to other methods.

Under the Internet of Things (IoT), the coexistence proof of multiple RFID tagged objects becomes a very useful mechanism in many application areas such as health care, evidences in court, and stores. The yoking-proof scheme addresses this issue. However, all existing yoking-proof schemes require two or more rounds communication to generate the yoking-proof. In this paper, we investigate the design of one-round yoking-proof schemes. Our contributions are threefold: (1) to confirm the coexistence of the RFID tag pair, we propose a one-round offline yoking-proof scheme with privacy protection. (2) We define a privacy model of the yoking-proof scheme and enhance Moriyama's security model for the yoking-proof scheme. The security and the privacy of the proposed scheme are proved under our models. (3) We further extend the yoking-proof scheme for the coexistence of m RFID tags, where m\textbackslashtextgreater2. The extended scheme maintains one-round. In addition, the proposed technique has efficiency advantage, compared with previous work.

Cloud computing is becoming the main computing model in the future due to its advantages such as high resource utilization rate and save high cost of performance. The public environments is become necessary to secure their storage and transmission against possible attacks such as known-plain-text attack and semantic security. How to ensure the data security and the privacy preserving, however, becomes a huge obstacle to its development. The traditional way to solve Secure Multiparty Computation (SMC) problem is using Trusted Third Party (TTP), however, TTPs are particularly hard to achieve and compute complexity. To protect user's privacy data, the encrypted outsourcing data are generally stored and processed in cloud computing by applying homomorphic encryption. According to above situation, we propose Elliptic Curve Cryptography (ECC) based homomorphic encryption scheme for SMC problem that is dramatically reduced computation and communication cost. It shows that the scheme has advantages in energy consumption, communication consumption and privacy protection through the comparison experiment between ECC based homomorphic encryption and RSA&Paillier encryption algorithm. Further evidence, the scheme of homomorphic encryption scheme based on ECC is applied to the calculation of GPS data of the earthquake and prove it is proved that the scheme is feasible, excellent encryption effect and high security.

Domain Name System (DNS) had been recognized as an indispensable and fundamental infrastructure of current Internet. However, due to the original design philosophy and easy access principle, one can conveniently wiretap the DNS requests and responses. Such phenomenon is a serious threat for user privacy protection especially when an inside hacking takes place. Motivated by such circumstances, we proposed a ports distribution management solution to relieve the potential information leakage inside local DNS. Users will be able to utilize pre-assigned port numbers instead of default port 53. Selection method of port numbers at the server side and interactive process with corresponding end host are investigated. The necessary implementation steps, including modifications of destination port field, extension option usage, etc., are also discussed. A mathematical model is presented to further evaluate the performance. Both the possible blocking probability and port utilization are illustrated. We expect that this solution will be beneficial not only for the users in security enhancement, but also for the DNS servers in resources optimization.

Privacy protection in Internet of Things (IoTs) has long been the topic of extensive research in the last decade. The perceptual layer of IoTs suffers the most significant privacy disclosing because of the limitation of hardware resources. Data encryption and anonymization are the most common methods to protect private information for the perceptual layer of IoTs. However, these efforts are ineffective to avoid privacy disclosure if the communication environment exists unknown wireless nodes which could be malicious devices. Therefore, in this paper we derive an innovative and passive method called Horizontal Hierarchy Slicing (HHS) method to detect the existence of unknown wireless devices which could result negative means to the privacy. PAM algorithm is used to cluster the HHS curves and analyze whether unknown wireless devices exist in the communicating environment. Link Quality Indicator data are utilized as the network parameters in this paper. The simulation results show their effectiveness in privacy protection.

Contactless communications have become omnipresent in our daily lives, from simple access cards to electronic passports. Such systems are particularly vulnerable to relay attacks, in which an adversary relays the messages from a prover to a verifier. Distance-bounding protocols were introduced to counter such attacks. Lately, there has been a very active research trend on improving the security of these protocols, but also on ensuring strong privacy properties with respect to active adversaries and malicious verifiers. In particular, a difficult threat to address is the terrorist fraud, in which a far-away prover cooperates with a nearby accomplice to fool a verifier. The usual defence against this attack is to make it impossible for the accomplice to succeed unless the prover provides him with enough information to recover his secret key and impersonate him later on. However, the mere existence of a long-term secret key is problematic with respect to privacy. In this paper, we propose a novel approach in which the prover does not leak his secret key but a reusable session key along with a group signature on it. This allows the adversary to impersonate him even without knowing his signature key. Based on this approach, we give the first distance-bounding protocol, called SPADE, integrating anonymity, revocability and provable resistance to standard threat models.

Protecting the privacy of user-identification data is fundamental to protect the information systems from attacks and vulnerabilities. Providing access to such data only to the limited and legitimate users is the key motivation for `Biometrics'. In `Biometric Systems' confirming a user's claim of his/her identity reliably, is more important than focusing on `what he/she really possesses' or `what he/she remembers'. In this paper the use of face image for biometric access is proposed using two multistage face recognition algorithms that employ biometric facial features to validate the user's claim. The proposed algorithms use standard algorithms and classifiers such as EigenFaces, PCA and LDA in stages. Performance evaluation of both proposed algorithms is carried out using two standard datasets, the Extended Yale database and AT&T database. Results using the proposed multi-stage algorithms are better than those using other standard algorithms. Current limitations and possible applications of the proposed algorithms are also discussed along, with further scope of making these robust to pose, illumination and noise variations.

The k-anonymity approach adopted by k-Same face de-identification methods enables these methods to serve their purpose of privacy protection. However, it also forces every k original faces to share the same de-identified face, making it impossible to track individuals in a k-Same de-identified video. To address this issue, this paper presents an approach to the creation of distinguishable de-identified faces. This new approach can serve privacy protection perfectly whilst producing de-identified faces that are as distinguishable as their original faces.

A process of de-identification used for privacy protection in multimedia content should be applied not only for primary biometric traits (face, voice) but for soft biometric traits as well. This paper deals with a proposal of the automatic hair color de-identification method working with video records. The method involves image hair area segmentation, basic hair color recognition, and modification of hair color for real-looking de-identified images.

Automated human facial image de-identification is a much needed technology for privacy-preserving social media and intelligent surveillance applications. Other than the usual face blurring techniques, in this work, we propose to achieve facial anonymity by slightly modifying existing facial images into "averaged faces" so that the corresponding identities are difficult to uncover. This approach preserves the aesthesis of the facial images while achieving the goal of privacy protection. In particular, we explore a deep learning-based facial identity-preserving (FIP) features. Unlike conventional face descriptors, the FIP features can significantly reduce intra-identity variances, while maintaining inter-identity distinctions. By suppressing and tinkering FIP features, we achieve the goal of k-anonymity facial image de-identification while preserving desired utilities. Using a face database, we successfully demonstrate that the resulting "averaged faces" will still preserve the aesthesis of the original images while defying facial image identity recognition.