Pseudo Identity Research Articles

Pseudo Identity in Cassava Rice as A Traditional Religious Identity Struggle Against Contemporary Religious Discrimination

The people of Cireundeu Village are known as a community group that still maintains the tradition of ancestral heritage of consuming rasi (cassava rice) as a staple food since 1918. This consumption process imbues rasi with the meaning that it is part of their culture. This study employed a qualitative approach with a semiotic method in decoding the meaning of rasi among the people of Cireundeu indigenous village. The purpose of this study was to find out 1) what meaning is constructed from rasi as their staple food 2) and what identity is built among the Cireundeu indigenous people. The results of this study show that 1) rasi is a form of struggle for religious identity that has been discriminated against, namely the struggle for belief in Sunda Wiwitan as their religious identity and 2) rasi as a group identity is a pseudo-identity of the Cireundeu indigenous people who primarily exist to maintain group beliefs in their beliefs.

Structured Domain Adaptation With Online Relation Regularization for Unsupervised Person Re-ID.

Unsupervised domain adaptation (UDA) aims at adapting the model trained on a labeled source-domain dataset to an unlabeled target-domain dataset. The task of UDA on open-set person reidentification (re-ID) is even more challenging as the identities (classes) do not have overlap between the two domains. One major research direction was based on domain translation, which, however, has fallen out of favor in recent years due to inferior performance compared with pseudo-label-based methods. We argue that domain translation has great potential on exploiting valuable source-domain data but the existing methods did not provide proper regularization on the translation process. Specifically, previous methods only focus on maintaining the identities of the translated images while ignoring the intersample relations during translation. To tackle the challenges, we propose an end-to-end structured domain adaptation framework with an online relation-consistency regularization term. During training, the person feature encoder is optimized to model intersample relations on-the-fly for supervising relation-consistency domain translation, which in turn improves the encoder with informative translated images. The encoder can be further improved with pseudo labels, where the source-to-target translated images with ground-truth identities and target-domain images with pseudo identities are jointly used for training. In the experiments, our proposed framework is shown to achieve state-of-the-art performance on multiple UDA tasks of person re-ID. With the synthetic→real translated images from our structured domain-translation network, we achieved second place in the Visual Domain Adaptation Challenge (VisDA) in 2020.

Visual Elements in Digital Technology Platforms and Visualized Communication in Spreading of False Information

Digital technology has demonstrated humongous benefits in spreading of information and awareness creation. Digital media platforms are observable facts that have turned out to be pervasive as the presence and use of digital platforms by netizens. Even more puzzling in several discourses is the near absence of the cultural strengthening and self-motivated of existence, particularly in Nigeria. This represents a new boundary of challenge. The use of visual elements in digital technology platforms and visualized communication in spreading of false information in Nigeria should be regulated. Its format is most used in promoting this practice. The major platforms used are WhatsApp, Facebook, YouTube and Instagram. This study utilized Survey design to obtain secondary data in literature and descriptive statistics were adopted using a self-structured questionnaire. The study adopts Technological Determinism theory and Disruptive Technology theory in observation of distinctiveness submissions to better knowledge of netizens behavioural pattern. Findings reveal that that digital technology user’s spread false information to gain attention, although significant confidence is placed on digital media platforms; many netizens hide their real identities. The study found out that false information spread through it creates panic and tension, pseudo identities dictate because sharing personal information violates long-standing culture of social identity secrecy entrenched in customary traditions in many parts of Nigeria. It was also found that emergence and continual spreading of false information need to be dealt with by imbibing societal value re-orientation for digital technology users; building their understanding and educating the masses. Spreading of false information has given rise to insecurity in Nigerian. Several unknown armed groups, tribal mercenaries and religious fundamentalist are proof of banditry and insecurity in Nigeria. Aside explaining themselves with overt and covert cultural distinctions of digital technology users, the study concludes that software engineers in Nigeria require developing modified software and re-categorizing bots accounts, and chat-bots especially into algorithmic template because of the socio-psychological peculiarities of the clime. The paper recommends the potential valuing and strengthening of digital technology platforms in ensuring reduction of spreading of false information in Nigeria.

Consistent Discrepancy Learning for Intra-Camera Supervised Person Re-Identification

Since annotating pedestrians across different views is extremely costly, intra-camera supervised person re-identification (ReID) aims to learn a ReID model from the intra-view labeled data. Under this setting, the most challenge lies in learning a view-invariant feature embedding in the absence of the cross-view annotations. Previous works focus on assigning a pseudo identity label for each image based on the feature similarity and learn view-invariant features by classification loss. However, because of the cross-view variations in lighting, background, etc., the pseudo labels are often noisy, and therefore not reliable for classification. In this paper, we explore learning a consistent discrepancy for pairwise images. Our main idea is that the discrepancy between pedestrian images should be consistent across different views regardless of view change so that it mainly depicts the identity difference. Due to the lack of cross-view annotations, we project images into different views and obtain likelihood prototypes for cross-view learning. These likelihood prototypes are used to measure the discrepancies between pairwise images under different views. And then, we propose an intra-view discrepancy preservation module to enforce the discrepancy to be view-consistent so as to encourage the model to distinguish the images based on the identities regardless of view change. Extensive experiments on multiple datasets show that our method outperforms existing related methods by clear margins and our method is comparable to supervised counterparts. Code will be made publicly available.

CREASE: Certificateless and REused-pseudonym based Authentication Scheme for Enabling security and privacy in VANETs

Due to the customers’ growing interest in using various intelligent and connected devices, we are surrounded by the Internet of Things (IoT). It is estimated that the number of IoT devices will exceed 60 billion by 2025. One of the primary reasons for such rapid growth is the Internet of Vehicles (IoV). Internet of Vehicles (IoV) has evolved into an emerging concept in intelligent transportation systems (ITS) that integrates VANETs and the IoT to enhance their capabilities. With the emergence of IoV and the interest shown by customers, Vehicular Ad hoc NETworks (VANETs) are likely to be widely deployed in the near future. However, for this to happen, wide participation of vehicle owners in VANET is needed. The primary concerns of vehicle owners to participate in VANET are privacy and security. In this paper, we present a Certificateless and REused-pseudonym based Authentication Scheme for Enabling security and privacy (CREASE) in VANETs. One of the ways to preserve the privacy of vehicles/drivers is to allow vehicles/drivers to use pseudo identities (pseudonyms) instead of their real identities (such as VIN number or driving license number) in all communications. The pseudonym used by a vehicle needs to be changed frequently to prevent the vehicle from being tracked. Our scheme uses Merkle Hash Tree and Modified Merkle Patricia Trie to efficiently store and manage the pseudonyms assigned to a vehicle. This enables a vehicle to pick and use a random pseudonym from a given set of pseudonyms assigned to it as well as change its pseudonym frequently and securely to ensure privacy. Unlike many of the existing schemes, our scheme does not use certificates and certificate revocation lists for authentication. Moreover, it allows vehicles to get a set of pseudonyms only once from the trusted authority. We present a formal proof of correctness of our scheme and also compare our scheme with some of the other contemporary schemes to show the effectiveness of our scheme.

ECDSA-based certificateless conditional privacy-preserving authentication scheme in Vehicular Ad Hoc Network

A Certificateless Public Key Cryptography (CL-PKC) aims to avoid the drawback of both traditional public key cryptography which requires a Public Key Infrastructure (PKI), and Identity-Based Public Key Cryptography (ID-PKC) which suffers from the inherent key escrow problem. In Vehicular Ad Hoc Network (VANET), a Certificateless Conditional Privacy Preserving Authentication (CCPPA) fulfills all known VANET security requirements in order to resolve challenges related to security and privacy. However, the existing certificateless schemes only achieve a trust level 2 according to the hierarchy defined by Girault. In case the partial private key is leaked, a malicious node can replace the public key since the public key is not bound to an identity. As a result, the trusted authority can only identify malicious nodes by using their pseudo identities. Al-Riyami and Paterson introduced a binding technique that allows to lift the trust level of a normal certificateless scheme to a trust level 3 by using a binding technique. This paper proposes new CCPPA schemes that use Al-Riyami and Paterson's technique, can achieve a stronger security and achieve a trust level 3. In case the partial private key is leaked, only the legitimate node can use the corresponding public key which has been certified by the trusted authority. As a result, the proposed CCPPA schemes address the vulnerability related to the public key replacement attack which is present in several CCPPA schemes. Our schemes also offer a tracing technique which consists of allowing the trusted authority to identify a malicious node using both its pseudo identity and public key. The proposed schemes use ECC cryptography and avoid Map-to-Hash function and bilinear pairing. Also, we called our schemes ECDSA-CCPPA and ECDSA*-CCPPA since they allow to respectively implement Elliptic Curve Digital Signature Algorithm (ECDSA) and the modified ECDSA* during authentication process in VANET. A security analysis is performed and proves that the ECDSA-CCPPA and ECDSA*-CCPPA schemes are secure in the random oracle. Actually, the ECDSA*-CCPPA scheme has an advantage over ECDSA-CCPPA in that it allows to perform a batch verification of signatures, where RSUs can support vehicles by collecting vehicle's signatures and verifying their messages in congested areas in order to reduce verification time. Additionally, a performance analysis is carried out to compare ECDSA*-CCPPA with several existing schemes. The simulation results show that ECDSA*-CCPPA outperforms the studied schemes with regard to the signature and verification process of one message. Moreover, it has the less overhead when compared to the studied certificateless schemes.

Offline-Online Associated Camera-Aware Proxies for Unsupervised Person Re-Identification.

Recently, unsupervised person re-identification (Re-ID) has received increasing research attention due to its potential for label-free applications. A promising way to address unsupervised Re-ID is clustering-based, which generates pseudo labels by clustering and uses the pseudo labels to train a Re-ID model iteratively. However, most clustering-based methods take each cluster as a pseudo identity class, neglecting the intra-cluster variance mainly caused by the change of cameras. To address this issue, we propose to split each single cluster into multiple proxies according to camera views. The camera-aware proxies explicitly capture local structures within clusters, by which the intra-ID variance and inter-ID similarity can be better tackled. Assisted with the camera-aware proxies, we design two proxy-level contrastive learning losses that are, respectively, based on offline and online association results. The offline association directly associates proxies according to the clustering and splitting results, while the online strategy dynamically associates proxies in terms of up-to-date features to reduce the noise caused by the delayed update of pseudo labels. The combination of two losses enables us to train a desirable Re-ID model. Extensive experiments on three person Re-ID datasets and one vehicle Re-ID dataset show that our proposed approach demonstrates competitive performance with state-of-the-art methods. Code will be available at: https://github.com/Terminator8758/O2CAP.

A Batch Authentication Design to Protect Conditional Privacy in Internet of Vehicles

Internet of vehicles (IoV), a novel technology, holds paramount importance within the transportation domain due to its ability to increase traffic efficiency and safety. Information privacy is of vital importance in IoV when sharing information among vehicles. However, due to the openness of the communication network, information sharing is vulnerable to potential attacks, such as impersonation, modification, side-channel and replay attacks, and so on. In order to resolve the aforementioned problem, we present a conditional privacy-preserving batch authentication (CPPBA) scheme based on elliptic curve cryptography (ECC). The proposed scheme avoids the certificate management problem, conducing to efficiency improvement. When a message is transmitted by a vehicle, its pseudo identity rather than the real identity is also broadcasted along with the shared message, which protects the privacy of the vehicle’s identity. But this privacy is conditional because TA and only the TA can reveal the real identity of the vehicle by tracing. The proposed scheme is batch verifiable, which reduces the computation costs. In addition, our scheme does not involve bilinear pairing operations and does not use the map-to-point hash function, thus making the verification process more effective. An exhaustive efficiency comparison has been carried to show that the proposed CPPBA scheme has lower computation, communication, and storage overheads than the state-of-the-art ones. A relatively comprehensive security analysis has also been carried, which not only shows that the signature design in the CPPBA scheme is unforgeable under the random oracle model but also illustrates that the CPPBA scheme is resistant to various potential attacks. The security is also verified by a popular automated simulation tool, that is, AVISPA.

Camera-Aware Proxies for Unsupervised Person Re-Identification

This paper tackles the purely unsupervised person re-identification (Re-ID) problem that requires no annotations. Some previous methods adopt clustering techniques to generate pseudo labels and use the produced labels to train Re-ID models progressively. These methods are relatively simple but effective. However, most clustering-based methods take each cluster as a pseudo identity class, neglecting the large intra-ID variance caused mainly by the change of camera views. To address this issue, we propose to split each single cluster into multiple proxies and each proxy represents the instances coming from the same camera. These camera-aware proxies enable us to deal with large intra-ID variance and generate more reliable pseudo labels for learning. Based on the camera-aware proxies, we design both intra and inter-camera contrastive learning components for our Re-ID model to effectively learn the ID discrimination ability within and across cameras. Meanwhile, a proxy-balanced sampling strategy is also designed, which facilitates our learning further. Extensive experiments on three large-scale Re-ID datasets show that our proposed approach outperforms most unsupervised methods by a significant margin. Especially, on the challenging MSMT17 dataset, we gain 14.3 percent Rank-1 and 10.2 percent mAP improvements when compared to the second place. Code is available at: https://github.com/Terminator8758/CAP-master.

A data-sharing scheme that supports multi-keyword search for electronic medical records.

As cloud storage technology develops, data sharing of cloud-based electronic medical records (EMRs) has become a hot topic in the academia and healthcare sectors. To solve the problem of secure search and sharing of EMR in cloud platforms, an EMR data-sharing scheme supporting multi-keyword search is proposed. The proposed scheme combines searchable encryption and proxy re-encryption technologies to perform keyword search and achieve secure sharing of encrypted EMR. At the same time, the scheme uses a traceable pseudo identity to protect the patient’s private information. Our scheme is proven secure based on the modified Bilinear Diffie-Hellman assumption and Quotient Decisional Bilinear Diffie-Hellman assumption under the random oracle model. The performance of our scheme is evaluated through theoretical analysis and numerical simulation.

Burnable Pseudo-Identity: A Non-Binding Anonymous Identity Method for Ethereum

The concept of identity has become one common research topic in security and privacy where the real identity of users must be preserved, usually covered by pseudonym identifiers. With the rise of Blockchain-based systems, identities are becoming even more critical than before, mainly due to the immutability property. In fact, many publicly accessible Blockchain networks like Ethereum rely on pseudonymization as a method for identifying subject actions. Pseudonyms are often employed to maintain anonymity, but true anonymity requires unlinkability. Without this property, any attacker can examine the messages sent by a specific pseudonym and learn new information about the holder of this pseudonym. This use of Blockchain collides with regulations because of the right to be forgotten, and Blockchain-based solutions are ensuring that every data stored within the chain will not be modified. In this paper we define a method and a tool for dealing with digital identities within Blockchain environments that are compliant with regulations. The proposed method provides a way to grant digital pseudo identities unlinked to the real identity. This new method uses the benefits of key derivation systems to ensure a non-binding interaction between users and the information model associated with their identity. The proposed method is demonstated in the Ethereum context and illustrated with a case study.

Privacy‐preserving cloud‐fog–based traceable road condition monitoring in VANET

SummaryWith the development of intelligent vehicles, the research on road condition monitoring has attracted much attention in the vehicular ad hoc network (VANET). The combination of VANET, cloud computing, and fog computing provides on‐demand computational resources while creating a lot of new challenges. In this paper, we propose an efficient privacy‐preserving cloud‐fog–based traceable road surface condition monitoring scheme. We use certificateless aggregate signcryption technology to implement multiple messages aggregation verification, which greatly saves computing resources and bandwidth. Moreover, we also use a traceable vehicle pseudo identity generated by a trace authority (TRA) to achieve identity privacy protection. To ensure the privacy of the fog and cloud server, the road condition information is reported in ciphertext format. In addition, the cloud server can perform ciphertext equivalence test operation to distinguish different road condition information of the same area without compromising the confidentiality. The ciphertext, which exceeds the set threshold, is uploaded to the blockchain. It can be stored permanently and never be tampered with or deleted. Finally, we demonstrate the correctness of the proposed scheme and show that the proposed scheme has higher efficiency.

HBLP: A Privacy Protection Framework for TIP Attributes in NTTP-Based LBS Systems

Nowadays, location-based services are being widely popularized due to their massive usage in current and emerging technologies. These services are based on searching out areas of interest which are likely to be accessed by users. Despite helping users worldwide, Location Based Services (LBSs) Systems endanger users' privacy because a user must provide personal information in order to use the services. Users thus become easy prey for assailants to access their social and personal lives. This problem is a giant issue for contemporary technologies because they are increasingly being used with the passage of time. Many existing solutions have attempted to resolve the challenges, but they face some serious dilemmas regarding the preservation of privacy. In order to address the privacy challenges in LBS systems, in this paper we have introduced a new Hierarchy Based Location Privacy (HBLP) model that protects the user's privacy, including the user's query time and identity and location information. The proposed model protects the user's privacy by using pseudo identity exchange, an aggregation protocol, and the concepts of Forest User (FU), Tree User (TU), and Child Users (CU) with k-anonymity and t-closeness, which is a reasonable combination for privacy provision for a user's query time, identity, and location. In order to evaluate the privacy protection level, we implemented the HBLP model in a Riverbed (Opnet) simulation and compared the results with existing state-of-the-art privacy-provisioning methods. The results showed that HBLP protected all the privacy attributes when a user interacts with an LBS system.

VTDP: Privately Sanitizing Fine-grained Vehicle Trajectory Data with Boosted Utility

With the rapidly growing deployment of intelligent transportation systems (ITS) and smart traffic applications, vehicle trajectory data are ubiquitously generated, e.g., from GPS navigation systems, mobile applications, and urban traffic cameras. Analyzing such fine-grained data would greatly benefit the development of ITS and smart cities, yet pose severe privacy risks due to the recorded drivers’ visited locations, routes, and driving habits. Recently, some privacy enhancing techniques were proposed to sanitize such data. However, such schemes have some major limitations–they either lack formal privacy notions to quantify and bound the privacy risks, or result in very limited utility, e.g., only a sequence of locations or aggregated information can be released (without retaining the speeds, accelerations and the timestamps of vehicles). In this article, we propose a novel framework to sanitize the fine-grained vehicle trajectories with differential privacy (VTDP), which provides rigorous privacy protection against adversaries who possess arbitrary background knowledge. Our VTDP technique involves three phases of differentially private sampling, which sequentially generate all the three categories of data (besides a pseudo identity for each vehicle)– position, moving, and timestamps . It also includes a vehicle trajectory interpolation procedure to further improve the output utility with the properties of fine-grained vehicle trajectory data. We conducted experiments on real vehicle trajectory datasets to validate the performance of our approach.

CL-CPPA: Certificate-Less Conditional Privacy-Preserving Authentication Protocol for the Internet of Vehicles

The conditional privacy-preserving authentication (CPPA) protocol has applications in the construction of secure Internet of Vehicles (IoV) due to its capability to achieve both privacy preservation and authentication simultaneously. We demonstrate the challenge in designing the secure CPPA protocols, e.g., the insecurity of master key, invalidity of pseudo-identity (PID), linkability, etc. Then, we present a certificate-less CPPA (CL-CPPA) protocol to be used in vehicular environments that supports both privacy and security requirements in the IoV system, where the vehicles and trusted authority (TA) does not need to store any certificates for verification and tracking, respectively. We also demonstrate that our proposed CL-CPPA protocol is secure against modification attacks, impersonation attacks, and other existing attacks. A comparative summary shows that our proposed CL-CPPA protocol has lower computation and communication costs in comparison to the state-of-the-art protocols.

A new and efficient authentication scheme for vehicular ad hoc networks

In the past decades, Vehicular Ad hoc Networks (VANETs) have been increasingly developed. Providing secure and efficient communication is essential in VANETs. One of most important challenges in the secure and efficient communications is proposing an appropriate authentication scheme. In this paper, we suggest an efficient and novel authentication scheme for VANETs. In the proposed scheme, vehicles authenticate each other without any limitation such as need for group of signers, online Road Side Units (RSUs), a set of pseudo identities and tamper-proof devices. Moreover, our simulation shows that the proposed scheme is well designed and efficient.

A secure lattice-based anonymous authentication scheme for VANETs

ABSTRACTIntelligent transportation systems (ITS) can improve the efficiency and safety of transportation. Vehicular Ad Hoc Networks (VANETs) are an important foundation of ITS. However, before being deployed on a large scale, VANETs should resolve the security and privacy issues generated from wireless communication. Digital signature has been used in VANETs to construct anonymous authentication schemes which can realize authentication and privacy preservation. Many previous anonymous authentication schemes have preloaded the master private key into a tamper-proof devices (TPD) which generate the pseudo identity and signature for each vehicle. However, tamper-proof devices cannot defend against side channel attacks. In order to realize better security, this paper takes advantage of lattice-based cryptography to design an anonymous authentication scheme without tamper-proof devices, which achieves security and privacy desired by VANETs. Furthermore, the anonymous authentication scheme lattice based also can defend against quantum computer attack. Finally, the security of this lattice-based anonymous authentication scheme is proven.

Anonymous Counting Problem in Trust Level Warning System for VANET

Using vehicular ad-hoc network, smart vehicles can detect dangerous events on the road and announce warnings to other vehicles to ensure road safety. The other vehicles nearby may receive the warning messages and accordingly choose a different route. But, because of the presence of malicious users on the road and errors in the detection system, some warning messages may prove dubious. Therefore, to distinguish between genuine and dubious messages, an element of trust has to be infused into the warning system. One common approach for this is to enable the receivers count the popularity of the reports of a warning event, i.e., the trust level. Once the trust level meets a predefined threshold, the vehicle's on-board unit will trust the warning event and warn the driver. Yet, owing to security and privacy concerns, anonymous counting problem does exist, because it is hard to fulfill both anonymity and counting requirements. In this paper, the authors define the anonymous counting problem and then propose a conditional distinguishable pseudo identities scheme that achieves conditional anonymity to overcome the counting problem. To achieve this feature of pseudo identities, time slot was added to minimize the influence of the adversary. Randomized batch verification was applied to enhance efficiency. Furthermore, it is shown how to achieve those features and provide proof on pairing equations. Finally, the time complexity of the proposed scheme was analyzed and its performance evaluated by Simulation of Urban Mobility.

Incorporating novel hierarchical secure model for performance and reliability evaluation in mobile agent system

Emerging mobile agent paradigm supports towards wider applications of mobile agent system (MAS). To guarantee security challenges due to several vulnerable threats into MAS, reliability issues are addressed. In this paper, a hierarchical secure model is designed for reliability prediction of MAS. The proposed security model is categorised as: host security, agent security and communication channel security. Security improvising effects over the reliability enhancements, security is maintained in mobile agent, host and during mobile agent migration. A novel multi-factor authentication is used at mobile agent verification; hosts are verified with respect to the certificate originality and pseudo identity. Opinions from neighbouring host are requested for secure migration of mobile agent. Security-based challenges solved in this paper shows enhancements in reliability of MAS that is estimated in terms of confidentiality, authentication at origin, integrity of agent, quality of mobile agent, security, fault tolerance, dynamic topology and link connectivity. Our proposed MAS are designed in NS3 simulator that results reliability enhancement according to number of mobile agents, size of MAS and number of hosts.

Incorporating novel hierarchical secure model for performance and reliability evaluation in mobile agent system

Emerging mobile agent paradigm supports towards wider applications of mobile agent system (MAS). To guarantee security challenges due to several vulnerable threats into MAS, reliability issues are addressed. In this paper, a hierarchical secure model is designed for reliability prediction of MAS. The proposed security model is categorised as: host security, agent security and communication channel security. Security improvising effects over the reliability enhancements, security is maintained in mobile agent, host and during mobile agent migration. A novel multi-factor authentication is used at mobile agent verification; hosts are verified with respect to the certificate originality and pseudo identity. Opinions from neighbouring host are requested for secure migration of mobile agent. Security-based challenges solved in this paper shows enhancements in reliability of MAS that is estimated in terms of confidentiality, authentication at origin, integrity of agent, quality of mobile agent, security, fault tolerance, dynamic topology and link connectivity. Our proposed MAS are designed in NS3 simulator that results reliability enhancement according to number of mobile agents, size of MAS and number of hosts.