Privacy Techniques Research Articles

System Model of VANET using Optimization-Based Efficient Routing Algorithm

A wireless networking strategy is the VANET (Vehicular ad hoc network). This approach is used to connect between vehicles and to raise the question of traffic quality. The most important aspect is the connection between V2v and V2i, so it is essential to ensure appropriate protection and privacy techniques. VANET safer, effective and avoid illegal vehicle interference with other cars, this work suggests an authentication scheme based on the hash feature. Only an authenticated vehicle may use the party key agreement scheme using the Chinese residual theorem. The forward and backward confidentiality was done in this manner. Security and privacy are fulfilled by our scheme. The most favorable aim is to create more useful, productive and safer roads across vehicle networks by educating the basic authorities and drivers in the future on a timely basis. Another aim is to identify wireless technology for the development of VANET. The aim is to protect and render feasible commercial requests by means of a variety of short to medium communication systems and/or other VANET networks. This technology will help the primary concern for safe contact in crucial time and meet the QOS criteria of other interactive multimedia applications or e-commerce.

A Novel and Efficient Priority-Based Cross-Layer Contextual Unobservability Scheme Against Global Attacks for WMSNs

Even though many security schemes proposed for wireless sensor networks protect transmitted data content from being revealed to different types of attacks and fulfill most of the desired security requirements, they are not addressing concealing the privacy of the contextual information. Contextual information such as event occurrence, event time, and event location can be exposed to an adversary by just monitoring network packet transmission. This kind of information is very important because it can leak location information of key nodes or even detected events themselves. Therefore, proposing a contextual unobservability scheme is a challenging task in sensor networks considering many issues: the broadcast nature of the wireless channel, the different attacker models, the network resource constraints, and the overhead on system performance. Most of the existing location privacy schemes are not addressing all these issues and are either not efficient against global adversaries or degrade significantly network performance. Thus in this paper, we propose an efficient location contextual anonymity scheme for Wireless Multimedia Sensor Network (WMSN) that exploits the cross-layer joint design among the application, routing, and MAC layers. Our proposed location unobservability scheme combines the source coding technique, probabilistic packet transmission, multipath routing, and priority-based dropping policy to enhance the efficiency level of the provided privacy service without noticeably affecting the Quality of Service (QoS) requirement for delivering multimedia content in WMSN. Performance evaluation results show that our proposed privacy mechanism outperforms other proposed location privacy techniques in terms of privacy efficiency (safety period) and network performance (end-to-end delay and energy consumption).

Multi-Access Edge Computing Architecture, Data Security and Privacy: A Review

Multi-Access Edge Computing (MEC) is an extension of cloud computing that aims to provide computation, storage, and networking capabilities at the edge of the network in close proximity to end-users. The MEC architecture supports applications and services that bridge between cloud computing and end-users. The architecture includes devices and systems that are interconnected, layered, and flexibly deployed. As a result of the technological advancements, MEC is facing a myriad of highly sophisticated threats. This paper provides a review of MEC Architecture, use cases, conceptual guidelines for MEC security architecture, security and privacy techniques, and identifies current and future challenges, their implications, and approaches to overcome the challenges. This research examined significant threats, described the MEC architecture, identified the susceptible functional layers, the different categories of threats, and the potential security safeguards. The research recommends that MEC providers should implement multiple layers of security controls to mitigate targeted attacks.

A Study on Role of Encryption Techniques for Data Privacy and Safety in MANETs

Mobile Ad hoc Network (MANET) is a self-configuring wireless network for versatility gadgets. It doesn't require any fixed framework to be arranged which makes it progressively reasonable to be utilized in conditions that require on-the-fly setup. Information security needs to regard three opposing solicitations: assurance of privacy, respectability and accessibility of information. The idea of building security of the information systems recognizes numerous methodologies. On the dimension of recognizable proof, verification and approval of clients, clients rights, confirmation and approval of clients data, numerous systems of conventions and applications have been created, which increment the dimension of security systems. Encryption is one of most promising mechanism that guarantees the data security and privacy for MANETs.

Linking Sensitive Data – Applications, Techniques, and Challenges

IntroductionThe linking of sensitive databases containing personal identifying information across organisations is an increasingly important task in application domains ranging from health and social science research to national censuses. Various techniques have been developed to facilitate the linking of sensitive databases while at the same time preserving the privacy of individuals represented in these databases.
 Objectives and approachWe present several case studies where the privacy-preserving linking of sensitive databases is crucial, and then discuss the advantages and limitations of existing algorithms and techniques to link sensitive databases. We discuss privacy techniques such as Bloom filter encoding, hashing, and secure multi-party computation, from the point of view of a linkage practitioner. We highlight those aspects that are important when selecting or implementing a privacy-preserving linkage technique within practical applications.
 ResultsConceptually, linkage techniques can be evaluated across three main dimensions linkage quality, scalability to linking large or multiple databases, and the privacy protection provided by a technique. From a practical perspective, however, several other dimensions are crucial, including the availability of software or ease of implementation, technical knowledge available in an organisation, and the suitability of techniques for a given linkage scenario. Our analysis of a diverse range of linkage techniques has shown that currently no technique provides an adequate solution along all conceptual as well as all practical dimensions.
 ConclusionsMore research is required to develop novel techniques that facilitate the privacy-preserving linkage of large sensitive databases across organisations; including new encoding methods and cryptanalysis attacks (where until now most attacks have neglected the attack vectors that likely occur in practice), and novel evaluation measures to assess the privacy provided by linkage techniques. We encourage practitioners to be aware of the identified limitations – as well as the opportunities – of existing privacy-preserving linkage techniques and carefully assess the technical and organisational requirements of such techniques within their institution.

SP-CIDS: Secure and Private Collaborative IDS for VANETs

Vehicular Ad hoc NETworks (VANETs) serve as the backbone of Intelligent Transportation Systems (ITS), providing passengers with safety and comfort. However, VANETs are vulnerable to major threats that affect data privacy and network services either from an individual or distributed attacker. In this paper, a Secure and Private-Collaborative Intrusion Detection System (SP-CIDS) is proposed to detect network attacks and to mitigate security concerns. In SP-CIDS, a Distributed Machine Learning (DML) model based on the Alternating Direction Method of Multipliers (ADMM) is used, which leverages the potential of vehicle-to-vehicle collaboration in the learning process to improve the storage efficiency, accuracy, and scalability of the IDS. However, there are significant data privacy concerns possible in such collaboration, where a CIDS can act as a malicious system that has access to the intermediate stages of the learning process. Additionally, the SP-CIDS system uses Differential Privacy (DP) technique to address the aforementioned data privacy risk associated with the DML-based CIDS. The SP-CIDS system is evaluated with logistic regression, naïve bayes, and ensemble classifiers. Simulation results substantiate that a private ensemble classifier secures the training data with DP and also achieves 96.94% accuracy.

Secure Elgamal Based Authentication Scheme for Cloud Assisted IOT Based Wireless Body Area Network

Now-a-days ireless Body Area Network (WBAN) is considered to be new era technique in which patient’s health record are monitored remotely by using wearable sensors from anywhere in the world. In such high-level communication, there is need of security services are required to protect the data being used by healthcare professionals and patients from intruders or attackers. Therefore, many researchers are showing their keen interest for security enhancement of WBAN architecture for secure communication. In this dissertation work, different security and privacy techniques are reviewed and analysed WBAN/IoT challenges as well their limitations based on the latest standards and publications. This research also covers the state-of-art security measures and research in WBAN. This research presents an ElGamal cryptosystem and biometric information authentication scheme for WBAN/IOT applications. This work observed that most of the authentication protocols using hash function and ElGamal cryptosystem for cloud-based applications are affected by security attacks and are unable to hide the actual identities of the end users during login session. Therefore, this work has introduced a secure biometric ElGamal-based authentication as well as data sharing schemes. The result analysis shows that the proposed work is better with respect to existing work with respect to execution time and cost as well as security level.

Review of Security and Privacy Scheme for Vehicular Ad Hoc Networks (VANETs)

Vehicles in a vehicular ad-hoc network (VANET) broadcast information about the driving environment in the road. Due to the open-access environment, this means that the VANET is susceptible to security and privacy issues. However, none of the related works satisfies all security and privacy requirements. Besides, their proposed has huge overhead in terms of computation and communication. The present paper is a provide a thorough background on VANETs and their entities; different security attacks; and all requirements of the privacy and security for VANETs. This paper may serve as a guide and reference for VANETs in the design and implementation of any new techniques for protection and privacy.

Parking recommender system privacy preservation through anonymization and differential privacy

AbstractRecent advancements in the Internet of Things (IoT) have enabled the development of smart parking systems that use services of third‐party parking recommender system to provide recommendations of personalized parking spot to users based on their past experience. However, the indiscriminate sharing of users' data with an untrusted (or semitrusted) parking recommender system may breach the privacy because users' behavior and mobility patterns could be inferred by analyzing their past history. Therefore, in this article, we present two solutions that preserve privacy of users in parking recommender systems while analyzing the past parking history using k‐anonymity (anonymization) and differential privacy (perturbation) techniques. Specifically, given an original parking database containing users' parking information, the k‐anonymity mechanism constructs an anonymized database, while differential privacy perturbs the query response using the Laplace mechanism, making the users indistinguishable in both approaches, hence preserving the privacy. Experimental results on a data set constructed from real parking measurements evaluate the trade‐off between privacy and utility, therefore enabling users to receive parking spots recommendations while preserving their privacy.

Conversion of Landsat 8 multispectral data through modified private content‐based image retrieval technique for secure transmission and privacy

AbstractIn this research work, we have developed a new image encryption‐based algorithm for Landsat 8 satellite images. Image statistical parameters related to first‐order image statistics, that is, mean (μ), SD (σ), and variance (σ2) are used to obtain the feature values for both original images and their encrypted versions. Multispectral satellite images generally contain a wide range of bands, which is 9 to 11 specifically for the Landsat 8 satellite. Hence, the information content of these images is richer than typical images. Through this algorithm, we aim to reduce the image content by randomly selecting only 10% of the total image pixels. These pixels are used to create new images, the so‐called encrypted images. The visual presentation of the encrypted image resembles a noisy picture. First‐order image statistics are calculated for both original images and their encrypted counterparts. These parameters show a similarity in their numerical values for both original and encrypted images. Thus, noisy pictures can be used for transmission in place of the original image when an outcome is decided through statistical image features. The visual similarity in the original images and encrypted images is obtained from the histogram signature plot for both original and encrypted images. The proposed algorithm can hence be used to convert image data for secure transmission and privacy.

Preserving Privacy Techniques for Autonomous Vehicles

Preserving Privacy Techniques for Autonomous Vehicles

Survey of Authentication and Privacy Schemes in Vehicular ad hoc Networks

Vehicular ad hoc networks (VANETs) have become increasingly common in recent years due to their critical role in the field of smart transportation by supporting Vehicle-to-Vehicle and Vehicle-to-Infrastructure communication. The security and privacy of VANET are of the utmost importance due to the use of an open wireless communication medium where messages are exchanged in plain text, something which allows attackers to intercept, tamper, replay, and delete them. Hence, there is a high probability that the safety of a VANET-based smart transportation system could be compromised. Nowadays, securing and safeguarding the exchange of messages in VANETs is the focus of many security research teams, as reflected by the number of authentication and privacy schemes that have been proposed. However, these schemes have not fulfilled all aspects of the security and privacy requirements. The present paper is an effort to provide a thorough background on VANETs and their components; various types of attacks on them; and all the security and privacy requirements for authentication and privacy schemes for VANETs. This paper is among the first to provide a comprehensive survey of the existing authentication and privacy schemes and compare them based on all security and privacy requirements, computational and communicational overheads, and the level of resistance to different types of attacks. It also provides a qualitative comparison with the existing surveys. This paper could serve as a guide and reference in the design and development of any new security and privacy techniques for VANETs.

Effects of differential privacy techniques: Considerations for end users.

We study the effects of differentially private (DP) noise injection techniques in a survey data setting, using the release of cost of early care and education estimates from the National Survey of Early Care and Education as a motivating example. As an example of how DP noise injection affects statistical estimates, our analysis compares the relative performance of DP techniques in the context of releasing estimates of means, medians, and regression coefficients. The results show that for many statistics, basic DP techniques show good performance provided that the privacy budget does not need to be split over too many estimates. Throughout, we show that small decisions, such as the number of bins in a histogram or the scaling of a variable in a regression equation, can have sometimes dramatic effects on the end results. Because of this, it is important to develop DP techniques with an eye towards the most important aspects of the data for end users.

A Differentially Private Game Theoretic Approach for Deceiving Cyber Adversaries

Cyber deception is one of the key approaches used to mislead attackers by hiding or providing inaccurate system information. There are two main factors limiting the real-world application of existing cyber deception approaches. The first limitation is that the number of systems in a network is assumed to be fixed. However, in the real world, the number of systems may be dynamically changed. The second limitation is that attackers' strategies are simplified in the literature. However, in the real world, attackers may be more powerful than theory suggests. To overcome these two limitations, we propose a novel differentially private game theoretic approach to cyber deception. In this proposed approach, a defender adopts differential privacy mechanisms to strategically change the number of systems and obfuscate the configurations of systems, while an attacker adopts a Bayesian inference approach to infer the real configurations of systems. By using the differential privacy technique, the proposed approach can 1) reduce the impacts on network security resulting from changes in the number of systems and 2) resist attacks regardless of attackers' reasoning power. The experimental results demonstrate the effectiveness of the proposed approach.

Efficient and Secure Outsourcing of Differentially Private Data Publishing With Multiple Evaluators

Since big data becomes a main impetus to the next generation of IT industry, data privacy has received considerable attention in recent years. To deal with the privacy challenges, differential privacy has been widely discussed and related private mechanisms are proposed as privacy-enhancing techniques. However, with today’s differential privacy techniques, it is difficult to generate a sanitized dataset that can suit every machine learning task. In order to adapt to various tasks and budgets, different kinds of privacy mechanisms have to be implemented, which inevitably incur enormous costs for computation and interaction. To this end, in this article, we propose two novel schemes for outsourcing differential privacy. The first scheme efficiently achieves outsourcing differential privacy by using our preprocessing method and secure building blocks. To support the queries from multiple evaluators, we give the second scheme that employs a trusted execution environment to aggregately implement privacy mechanisms on multiple queries. During data publishing, our proposed schemes allow providers to go off-line after uploading their datasets, so that they achieve a low communication cost which is one of the critical requirements for a practical system. Finally, we report an experimental evaluation on UCI datasets, which confirms the effectiveness of our schemes.

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such as privacy violations, security issues and model fairness. Differential privacy, as a promising mathematical model, has several attractive properties that can help solve these problems, making it quite a valuable tool. For this reason, differential privacy has been broadly applied in AI but to date, no study has documented which differential privacy mechanisms can or have been leveraged to overcome its issues or the properties that make this possible. In this paper, we show that differential privacy can do more than just privacy preservation. It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI. With a focus on regular machine learning, distributed machine learning, deep learning, and multi-agent systems, the purpose of this article is to deliver a new view on many possibilities for improving AI performance with differential privacy techniques.

A Secure Federated Learning Framework for 5G Networks

Federated Learning (FL) has been recently proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing privacy preservation for participants. In FL, the central aggregator accumulates local updates uploaded by participants to update a global model. However, there are two critical security threats: poisoning and membership inference attacks. These attacks may be carried out by malicious or unreliable participants, resulting in the construction failure of global models or privacy leakage of FL models. Therefore, it is crucial for FL to develop security means of defense. In this article, we propose a blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from involving in FL. In doing so, the central aggregator recognizes malicious and unreliable participants by automatically executing smart contracts to defend against poisoning attacks. Further, we use local differential privacy techniques to prevent membership inference attacks. Numerical results suggest that the proposed framework can effectively deter poisoning and membership inference attacks, thereby improving the security of FL in 5G networks.

Certificateless Proxy Reencryption Scheme (CPRES) Based on Hyperelliptic Curve for Access Control in Content-Centric Network (CCN)

Information-centric networking is the developing model envisioned by an increasing body of the data communication research community, which shifts the current network paradigm from host centric to data centric, well-known to information-centric networking (ICN). Further, the ICN adopts different types of architectures to extend the growth of the Internet infrastructure, e.g., name-based routing and in-network caching. As a result, the data can be easily routed and accessed within the network. However, when the producer generates contents for authentic consumers, then it is necessary for him/her to have a technique for content confidentiality, privacy, and access control. To provide the previously mentioned services, this paper presents a certificateless proxy reencryption scheme (CPRES) based on the hyperelliptic curve for access control in the content-centric network (CCN). Using certificateless PRE, the power of the key generation center (KGC) is limited to only the generation of partial keys to secure the access to the content. With the help of these partial keys, the producer further calculates keys for encryption and reencryption process. The simulation results show that the proposed scheme provides secure access to content during end-to-end communication. Moreover, the proposed CPRES scheme outperforms in terms of low computational energy and efficient utilization of communication bandwidth.

Practical Privacy Preserving POI Recommendation

Point-of-Interest (POI) recommendation has been extensively studied and successfully applied in industry recently. However, most existing approaches build centralized models on the basis of collecting users’ data. Both private data and models are held by the recommender, which causes serious privacy concerns. In this article, we propose a novel Privacy preserving POI Recommendation (PriRec) framework. First, to protect data privacy, users’ private data (features and actions) are kept on their own side, e.g., Cellphone or Pad. Meanwhile, the public data that need to be accessed by all the users are kept by the recommender to reduce the storage costs of users’ devices. Those public data include: (1) static data only related to the status of POI, such as POI categories, and (2) dynamic data dependent on user-POI actions such as visited counts. The dynamic data could be sensitive, and we develop local differential privacy techniques to release such data to the public with privacy guarantees. Second, PriRec follows the representations of Factorization Machine (FM) that consists of a linear model and the feature interaction model. To protect the model privacy, the linear models are saved on the users’ side, and we propose a secure decentralized gradient descent protocol for users to learn it collaboratively. The feature interaction model is kept by the recommender since there is no privacy risk, and we adopt a secure aggregation strategy in a federated learning paradigm to learn it. To this end, PriRec keeps users’ private raw data and models in users’ own hands, and protects user privacy to a large extent. We apply PriRec in real-world datasets, and comprehensive experiments demonstrate that, compared with FM, PriRec achieves comparable or even better recommendation accuracy.

A Neuron Noise-Injection Technique for Privacy Preserving Deep Neural Networks

AbstractData is the key to information mining that unveils hidden knowledge. The ability to revealed knowledge relies on the extractable features of a dataset and likewise the depth of the mining model. Conversely, several of these datasets embed sensitive information that can engender privacy violation and are subsequently used to build deep neural network (DNN) models. Recent approaches to enact privacy and protect data sensitivity in DNN models does decline accuracy, thus, giving rise to significant accuracy disparity between a non-private DNN and a privacy preserving DNN model. This accuracy gap is due to the enormous uncalculated noise flooding and the inability to quantify the right level of noise required to perturb distinct neurons in the DNN model, hence, a dent in accuracy. Consequently, this has hindered the use of privacy protected DNN models in real life applications. In this paper, we present a neuron noise-injection technique based on layer-wise buffered contribution ratio forwarding and ϵ-differential privacy technique to preserve privacy in a DNN model. We adapt a layer-wise relevance propagation technique to compute contribution ratio for each neuron in our network at the pre-training phase. Based on the proportion of each neuron’s contribution ratio, we generate a noise-tuple via the Laplace mechanism, and this helps to eliminate unwanted noise flooding. The noise-tuple is subsequently injected into the training network through its neurons to preserve privacy of the training dataset in a differentially private manner. Hence, each neuron receives right proportion of noise as estimated via contribution ratio, and as a result, unquantifiable noise that drops accuracy of privacy preserving DNN models is avoided. Extensive experiments were conducted based on three real-world datasets and their results show that our approach was able to narrow down the existing accuracy gap to a close proximity, as well outperforms the state-of-the-art approaches in this context.