Content Privacy Research Articles

Achieving the Optimal k-Anonymity for Content Privacy in Interactive Cyberphysical Systems

Modern applications and services leveraged by interactive cyberphysical systems (CPS) are providing significant convenience to our daily life in various aspects at present. Clients submit their requests including query contents to CPS servers to enjoy diverse services such as health care, automatic driving, and location-based services. However, privacy concerns arise at the same time. Content privacy is recognized and a lot of efforts have been made in the literature of privacy preserving in interactive cyberphysical systems such as location-based services. Nevertheless, neither the cloaking based solutions nor existing client based solutions have achieved effective content privacy by optimizing proper content privacy metrics. In this paper we formulate the problem of achieving the optimal content privacy in interactive cyberphysical systems using k-anonymity solutions based on two content privacy metrics, which are defined using the concepts of entropy and differential privacy. Then we propose an algorithm, Multilayer Alignment (MLA), to establish k-anonymity mechanisms for preserving content privacy in interactive cyberphysical systems. Our proposed MLA is theoretically proved to achieve the optimal content privacy in terms of both the entropy based and the differential privacy mannered content privacy metrics. Evaluation based on real-life datasets is conducted, and the evaluation results validate the effectiveness of our proposed algorithm.

Messages in a Concealed Bottle: Achieving Query Content Privacy With Accurate Location-Based Services

Query privacy has led to a serious concern for location-based services (LBSs). Existing mainstream schemes preserve query privacy by either introducing a third party or degrading the accuracy of LBS. To address these deficiencies, we propose a scheme named <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the content sealed bottle</i> in this paper for the users to get accurate LBSs while the server learns zero knowledge on the query content. To realize this objective, the Euclidean distance based on homomorphic encryption is used to signal the difference between the query attribute vector of a user and each database attribute vector of the server; then, a Boolean circuit is designed to identify the exact match for the query attribute vector. By optimizing the traditional subcircuits, the total number of non-XOR gates, which is the main cost during the matching process, can be reduced by a factor of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> compared to that of the traditional <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -to-1 MIN subcircuit, where <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> is the input bit length of the Boolean circuit. Finally, the computationally private information retrieval technique is employed to privately retrieve the points-of-interest set. To the best of our knowledge, our <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">content sealed bottle</i> is the first scheme that can achieve cost-effective query privacy preservation without the involvement of a third party or sacrificing the accuracy of LBSs. Our security analysis and extensive experimental results demonstrate the effectiveness of the proposed scheme.

Blockchain‐based fair three‐party contract signing protocol for fog computing

SummaryFog computing is a new computing paradigm that can provide flexible resources and services at the edge of network. It is an extension of cloud computing and usually cooperated with cloud computing. Therefore, end users, fog nodes, and cloud servers can form a three‐layer service model in practical application. In this model, they should have an agreement on a service contract, which contains every party's rights and obligations before the beginning of the service. However, due to lack of trust, it will suffer from some fairness problems during signing a service contract. Contract signing protocol allows two or more mutual distrust entities to sign a predefined digital contract in a fair and effective way. In this paper, we propose a fair three‐party contract signing protocol based on the primitive of blockchain, which can be applied to the scenario of fog computing. Our proposed construction allows the participants to sign a contract in a fair way without the involvement of an arbitrator. Moreover, the privacy of the contract content can be preserved on the public chain. Finally, we realize the proposed protocol through the private blockchain and provide the experimental simulation that analyzes the efficiency and effectiveness.

Privacy-Preserving Image Processing in the Cloud

Millions of private images are generated in various digital devices every day. The consequent massive computational workload makes people turn to cloud computing platforms for their economical computation resources. Meanwhile, the privacy concerns over the sensitive information contained in outsourced image data arise in public. In fact, once uploaded to cloud, the security and privacy of the image content can only presume upon the reliability of the cloud service providers. Lack of assuring security and privacy guarantees becomes the main barrier to further deployment of cloud-based image processing systems. This paper studies the design targets and technical challenges lie in constructing cloud-based privacy-preserving image processing system. We explore various image processing tasks, including image feature detection, digital watermarking, content-based image search. The state-of-the-art techniques, including secure multiparty computation, and homomorphic encryption are investigated. A detailed taxonomy of the problem statement and the corresponding solutions is provided

Freedom of Thought and Mental Integrity: The Moral Requirements for Any Neural Prosthesis.

There are many kinds of neural prostheses available or being researched today. In most cases they are intended to cure or improve the condition of patients affected by some cerebral deficiency. In other cases, their goal is to provide new means to maintain or improve an individual's normal performance. In all these circumstances, one of the possible risks is that of violating the privacy of brain contents (which partly coincide with mental contents) or of depriving individuals of full control over their thoughts (mental states), as the latter are at least partly detectable by new prosthetic technologies. Given the (ethical) premise that the absolute privacy and integrity of the most relevant part of one's brain data is (one of) the most valuable and inviolable human right(s), I argue that a (technical) principle should guide the design and regulation of new neural prostheses. The premise is justified by the fact that whatever the coercion, the threat or the violence undergone, the person can generally preserve a “private repository” of thought in which to defend her convictions and identity, her dignity, and autonomy. Without it, the person may end up in a state of complete subjection to other individuals. The following functional principle is that neural prostheses should be technically designed and built so as to prevent such outcomes. They should: (a) incorporate systems that can find and signal the unauthorized detection, alteration, and diffusion of brain data and brain functioning; (b) be able to stop any unauthorized detection, alteration, and diffusion of brain data. This should not only regard individual devices, but act as a general (technical) operating principle shared by all interconnected systems that deal with decoding brain activity and brain functioning.

Privacy-preserving sparse representation classification in cloud-enabled mobile applications

Mobile devices are now pervasive to provide prolific services to the users meanwhile collect the information derived from the activities of the individuals with the onboard sensors. Classification and authentication are popular provided services of many mobile applications which have high probability to involve the sensitive information of the users. In this paper, we propose a new cloud-enabled and Privacy-preserving sparse representation classification (P2-SRC) system to protect the privacy of both the “data contributors” and “application users” when cloud server is untrusted. Different from the state-of-the-art approaches which only consider the attacks on data values, our proposed system, P2-SRC, addresses multiple types of privacy attacks including Content Privacy Attacks, Source Privacy Attacks and Label Privacy Attacks. As a result, besides the data values, in P2-SRC, the identities and activities of the users are also protected. According to our evaluations on two different classification applications (face recognition and activity recognition), P2-SRC achieves almost the same classification accuracy compared with traditional SRC approach which indicates the security add-ons do not affect the accuracy of the SRC classifier. We also demonstrate that it outperforms the most related work, Pickle, significantly on recognition accuracy and privacy protections. Meanwhile the implementation of P2-SRC in a face recognition application on smartphones demonstrates that P2-SRC based authentication system accounts for only 0.000041% of the total energy supply of a normal smartphone and the average responding time is around 1.1 s for each recognition request.

A Secure and Efficient Privacy-Preserving Range Query Scheme in Location-Based Services

With the rapid development of wireless communications and mobile devices with location capability, location-based services (LBS) have been extensively used in almost all social and business domains. In terms of privacy and efficiency, there are two main limitations in the existing work that have to be addressed. First, they fail to preserve the location and content privacy simultaneously. Second, they do not support the range queries. In this paper, we propose a secure and efficient privacy-preserving range query protocol in the LBS, which satisfies the three requirements of the location privacy and content privacy protection, query efficiency, and scalable index size. To achieve location privacy and content privacy, we adopt the prefix membership verification scheme to encode index elements and a bloom filter to store index elements. To achieve query efficiency and scalable index size, a balanced dynamic binary tree structure called $DBtree$ is proposed. Furthermore, to improve the query efficiency, we propose both the index element encoding optimization and traversal optimization algorithms. Finally, we evaluate our scheme both on the simulated and real-world datasets. The experimental results demonstrate the security and efficiency of our scheme.

An information discovery scheme for IoS of agricultural services using improved chord-ONS model

To solve the problems in EPC early network discovery service mode, we have analysed many aspects, such as load balance, robustness, and content privacy. We explain the architecture and workflow of structured P2P based ONS resolution system, and analyse advantages and disadvantages of current P2P-ONS system. Then the improved scheme is put forward which adopts hierarchical P2P network architecture to form ONS resolution system. It proposes a two-level chord-ONS architecture including routing performance analysis, storage and query of system information, nodes joining in or out, and selection of super nodes. The routing strategy based on node performance for super nodes and common nodes is also put forward. By the analysis of simulation results we find that the improved routing mechanism is better than the structured chord routing mechanism in query hopping, query delay and stability. It is more suitable for internet of things with huge dynamic and huge amount of information and it also shows comprehensive performance compared to traditional structured P2P network.

Security, Privacy, and Access Control in Information-Centric Networking: A Survey

Information-centric networking (ICN) replaces the widely used host-centric networking paradigm in communication networks (e.g., Internet and mobile ad hoc networks) with an information-centric paradigm, which prioritizes the delivery of named content, oblivious of the contents’ origin. Content and client security, provenance, and identity privacy are intrinsic by design in the ICN paradigm as opposed to the current host centric paradigm where they have been instrumented as an after-thought. However, given its nascency, the ICN paradigm has several open security and privacy concerns. In this paper, we survey the existing literature in security and privacy in ICN and present open questions. More specifically, we explore three broad areas: 1) security threats; 2) privacy risks; and 3) access control enforcement mechanisms. We present the underlying principle of the existing works, discuss the drawbacks of the proposed approaches, and explore potential future research directions. In security, we review attack scenarios, such as denial of service, cache pollution, and content poisoning. In privacy, we discuss user privacy and anonymity, name and signature privacy, and content privacy. ICN’s feature of ubiquitous caching introduces a major challenge for access control enforcement that requires special attention. We review existing access control mechanisms including encryption-based, attribute-based, session-based, and proxy re-encryption-based access control schemes. We conclude the survey with lessons learned and scope for future work.

Designing Privacy-Preserving Protocols for Content Sharing and Aggregation in Content Centric Networking

In the content-centric networking (CCN), the content transmitted between data owners, consumers, and the servers is confidential to its publishers. Therefore, the content’s publishers are sensitive to publication of their content to unauthorized consumers and third party service providers. Access control-based mechanisms have been efficient solutions to address this challenge where the content can be accessed by legitimate consumers only. However, the main limitation with an access control-based solution is that the content itself and the user interests are made public to certain parties in the system. Moreover, the solutions based on access control mechanism are limited within only sharing the content among various publishers and consumers where on the contrary, the secure aggregation of content in CCN is yet to be explored. To address these issues, we describe possible cryptographic solutions for content privacy in CCN, which can share and aggregate the content securely. More specifically, we propose two cryptographic protocols for content sharing and aggregation: 1) cryptographic protocol to exchange the encrypted content (CPE2C) and 2) privacy-preserving aggregation over distributed content (PDAC). The CPE2C protocol is simple yet highly secure against intruders or collusion attacks. This protocol is useful in terms of exchanging sensitive content between a publisher and a consumer. The PDAC is effective for distributed, secure, and energy-efficient content aggregation in smart IoT systems leveraging the CCN architecture and cloud services. The privacy analysis and performance results show that the proposed cryptographic protocols perform efficiently without disclosing any private information.

Towards efficient privacy-preserving encrypted image search in cloud computing

With the rapid development of cloud computing and big data, it is an ever-increasing trend to outsource the huge amount of multimedia data to the cloud. Although data outsourcing can alleviate data maintenance cost of user side, it inevitably brings some new security challenges, of which privacy of image content is the most significant one. Thus, privacy-preserving image search becomes a new research interest in academic community. In this paper, we propose a novel efficient search scheme for encrypted image by incorporating K-means and secure modular hashing, which can simultaneously achieve privacy-preserving of image search and comparable search accuracy to plaintext image. Furthermore, we provide comprehensively security analysis on the proposed solution and detailed efficiency evaluation over some real-world datasets. The results show that our solution can achieve desired security goals while providing a comparable search efficiency and accuracy.

3D textured model encryption via 3D Lu chaotic mapping

In the emerging Virtual/Augmented Reality (VR/AR) era, three dimensional (3D) content will be popularized just as images and videos today. The security and privacy of these 3D contents should be taken into consideration. 3D contents contain surface models and solid models. Surface models include point clouds, meshes and textured models. Previous work mainly focused on the encryption of solid models, point clouds and meshes. This work focuses on the most complicated 3D textured model. We propose a 3D Lu chaotic mapping based encryption method for 3D textured models. We encrypt the vertices, polygons, and textures of 3D models separately using the 3D Lu chaotic mapping. Then the encrypted vertices, polygons and textures are composited together to form the final encrypted 3D textured model. The experimental results reveal that our method can encrypt and decrypt 3D textured models correctly. Furthermore, typical statistic and brute-force attacks can be resisted by the proposed method.

A framework for secure and privacy protected collaborative contents sharing using public OSN

A major issue thoroughly raised and potentially vulnerable in online social networks (OSNs) is the user privacy preservation. Generally, the issue of privacy is tackled from the user point of view without considering the service providers and the third-party data collectors, who can manipulate user data for third party advertisement and behavioral analysis. Therefore, the privacy risk exists not only from the unauthorized users but also from the OSN service providers. To secure data from both the unauthorized users and OSN service providers a framework for collaborative contents sharing is proposed. In the proposed framework, contents are secured at the time of data dissemination and provided assurance about the data sharing among the legitimate users based on collaborative contents sharing. The proposed framework assures not only the confidentiality of contents but also the privacy of data owner and co-owners in OSN. An architecture is provided to support the theoretical and practical basis for the proposed framework; which exhibits the objective of preserving user and contents privacy.

Control Cloud Data Access Privilge Anonymity with Attributed Based Encryption

Cloud computing is a revolutionary computing paradigm, which enables flexible, on-demand, and low-cost usage of computing resources, but the data is outsourced to some cloud servers, and various privacy concerns emerge from it. Various schemes based on the attribute-based encryption have been to secure the cloud storage. Data content privacy. A semi anonymous privilege control scheme AnonyControl to address not only the data privacy. But also the user identity privacy. AnonyControl decentralizes the central authority to limit the identity leakage and thus achieves semi anonymity. The Anonymity –F which fully prevent the identity leakage and achieve the full anonymity.

Text message reminders for improving patient appointment adherence in an office-based buprenorphine program: A feasibility study.

Missed visits are common in office-based buprenorphine treatment (OBOT). The feasibility of text message (TM) appointment reminders among OBOT patients is unknown. This 6-month prospective cohort study provided TM reminders to OBOT program patients (N = 93). A feasibility survey was completed following delivery of TM reminders and at 6 months. Respondents reported that the reminders should be provided to all OBOT patients (100%) and helped them to adhere to their scheduled appointment (97%). At 6 months, there were no reports of intrusion to their privacy or disruption of daily activities due to the TM reminders. Most participants reported that the TM reminders were helpful in adhering to scheduled appointments (95%), that the reminders should be offered to all clinic patients (95%), and favored receiving only TM reminders rather than telephone reminders (95%). Barriers to adhering to scheduled appointment times included transportation difficulties (34%), not being able to take time off from school or work (31%), long clinic wait-times (9%), being hospitalized or sick (8%), feeling sad or depressed (6%), and child care (6%). This study demonstrated the acceptability and feasibility of TM appointment reminders in OBOT. Older age and longer duration in buprenorphine treatment did not diminish interest in receiving the TM intervention. Although OBOT patients expressed concern regarding the privacy of TM content sent from their providers, privacy issues were uncommon among this cohort. Scientific Significance Findings from this study highlighted patient barriers to adherence to scheduled appointments. These barriers included transportation difficulties (34%), not being able to take time off from school or work (31%), long clinic lines (9%), and other factors that may confound the effect of future TM appointment reminder interventions. Further research is also required to assess 1) the level of system changes required to integrate TM appointment reminder tools with already existing electronic medical records and appointment records software; 2) acceptability among clinicians and administrators; and 3) financial and resource constraints to healthcare systems. (Am J Addict 2017;26:581-586).

Reliable Relay: Autonomous Social D2D Paradigm for 5G LoS Communications

Next generation 5G networks contemplate to exploit millimeter Wave (mmWave) for a massive increase in data rates but its inherent characteristic of line-of-sight (LoS) brings up the challenge of directional synchronization of transmitters and receivers. In this letter, we propose a fusion of social Internet-of-Things and device-to-device (D2D) communications, shaping social D2D to tackle mmWave LoS challenge using an independent “reliable relay” scheme. We introduce distributed social network of devices and propose social-communication graph formation and autonomous trustworthy relay procedure. Utilizing reliable relay, a non-LoS device can communicate to 5G networks without compromising the contents privacy. Our analysis demonstrates that agile reliable relay substantially improves the capacity gain and data rate and can be an integral part of existing relay selection schemes. NS3-based simulations validate the proliferation in throughput with minimum additional delays.

Biometric Security Through Visual Encryption for Fog Edge Computing

Fog and mobile edge computing have gained considerable attention from the research and development community. The problems related to security and privacy of biometric content are simpler to solve through edge computing resulting in improved security and privacy of biometric and other critically private information. Zero-watermarking has been proposed as a solution to help protect the ownership of multimedia content that is easy to copy and distribute. Visual cryptography is another approach to secure data that is to be shared through generating multiple shares. This paper is concerned with developing a biometric security solution for face images, using visual cryptography and zero-watermarking, that does not adversely impact the visual quality of the image. The original face image is not modified through the zero-watermarking and visual encryption procedures and this in turn does not adversely impact the recognition rate.

Increasing Availability, Data Privacy and Copyrights of Digital Content via a Pilot Project of the National Library of Finland

Availability, data privacy and copyrights are topics which the National Library of Finland (NLF) is currently working on to increase the availability of digital content. Currently, in Finland, the digital collection of newspapers, journals and ephemera at the digi.kansalliskirjasto.fi service is available for everyone, where, e.g., newspapers are available for the years 1771-1910. The material after that period is visible in the six legal deposit libraries all over Finland. Researchers, especially, but also other customer segments are demanding better access to digital content.&nbsp; We at the NLF are searching for ways to open the collections to wider audiences.&nbsp; Therefore, we have established a pilot project, where we are experimenting with new modes of operation in opening digital content for education and research. In the pilot project, Aviisi, the participants are media houses, copyright organisations, universities, schools, public libraries, archives, museums and research organisations. We are creating tailored contract models to improve our responsiveness to copyright and data privacy issues. Beside the contract modes, we are also working with the technical solutions and communicating with the participants about possible ways to utilise digital content. Our objective is that, by the end of the project, we will have clarified legal, technical and availability questions, so that we will be prepared for most common scenarios and can proceed with the pilot projects, and then onwards after the overall success of all our pilot projects. Currently, we have fifteen pilot projects (hereafter, pilots) running, which cover about 40,000 people in the Mikkeli region, where about 4,000 of those people are directly involved in the pilots as an estimate of students, teachers, researchers or other contact points. In this paper, we will discuss the pilots, how we have approached them to use digital content and attempted to increase the availability to the content overall while honouring the regulations of data privacy and the rights of copyright holders. In the last few months of 2016, as we finalise the project, we will aim to take the learning forward into working models, which will pave the way to further expanding the concept.

Image steganography using uncorrelated color space and its application for security of visual contents in online social networks

Image steganography is a growing research field, where sensitive contents are embedded in images, keeping their visual quality intact. Researchers have used correlated color space such as RGB, where modification to one channel affects the overall quality of stego-images, hence decreasing its suitability for steganographic algorithms. Therefore, in this paper, we propose an adaptive LSB substitution method using uncorrelated color space, increasing the property of imperceptibility while minimizing the chances of detection by the human vision system. In the proposed scheme, the input image is passed through an image scrambler, resulting in an encrypted image, preserving the privacy of image contents, and then converted to HSV color space for further processing. The secret contents are encrypted using an iterative magic matrix encryption algorithm (IMMEA) for better security, producing the cipher contents. An adaptive LSB substitution method is then used to embed the encrypted data inside the V-plane of HSV color model based on secret key-directed block magic LSB mechanism. The idea of utilizing HSV color space for data hiding is inspired from its properties including de-correlation, cost-effectiveness in processing, better stego image quality, and suitability for steganography as verified by our experiments, compared to other color spaces such as RGB, YCbCr, HSI, and Lab. The quantitative and qualitative experimental results of the proposed framework and its application for addressing the security and privacy of visual contents in online social networks (OSNs), confirm its effectiveness in contrast to state-of-the-art methods.

"I Always Vet Things": Navigating Privacy and the Presentation of Self on Health Discussion Boards Among Individuals with Long-Term Conditions.

BackgroundThe ethics of research into online communities is a long-debated issue, with many researchers arguing that open-access discussion groups are publically accessible data and do not require informed consent from participants for their use for research purposes. However, it has been suggested that there is a discrepancy between the perceived and actual privacy of user-generated online content by community members.ObjectiveThere has been very little research regarding how privacy is experienced and enacted online. The objective of this study is to address this gap by qualitatively exploring the expectations of privacy on Internet forums among individuals with long-term conditions.MethodsSemistructured interviews were conducted with 20 participants with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and 21 participants with type 1 and 2 diabetes mellitus, and were analyzed using thematic analysis. Participants were recruited via online and offline routes, namely forums, email lists, newsletters, and face-to-face support groups.ResultsThe findings indicate that privacy online is a nebulous concept. Rather than individuals drawing a clear-cut distinction between what they would and would not be comfortable sharing online, it was evident that these situations were contextually dependent and related to a number of unique and individual factors.ConclusionsInterviewees were seen to carefully manage how they presented themselves on forums, filtering and selecting the information that they shared about themselves in order to develop and maintain a particular online persona, while maintaining and preserving an acceptable level of privacy.