An efficient privacy protection scheme for data security in video surveillance

RFID in the Information Security and Privacy is hidden in a crisis. RFID Information Security and Privacy Protection using hardware ways to achieve the target is the best way, however, it is subject to the problem of Tag costs and capacity and is currently still unresolved issues. This study focus on the Information Security and Privacy Protection and put forth a EPC specifications to use the model of XOR logic operation with the method of CRC examining the computing, and simulating the shopping mall system which provides a Information Security and Privacy Protection system. We got the data after simulations to show that XOR computing to actual test of 8 bytes, the required time is 17.29 Ticks in average, the spend time is rather short. Follow-up our simulations to the test data of DES encryption methods, an average time of 8,039.41 Ticks is required, the two time required considerable differences. It was informed that this study through practical shows that we could provide a match hardware encryption methods of the specific solutions to achieve their personal privacy protection, confidentiality, confirmed, and the fulfillment of the Information Security and Privacy Protection.

Intelligent Video surveillance systems, leveraging edge computing, have become increasingly prevalent in various facilities, providing advanced monitoring and management capabilities. However, these systems can inadvertently compromise personally identifiable information, such as human images, leading to privacy violations. We introduced a semantic privacy-preserving video surveillance service on the edge to address this critical issue. Unlike traditional centralized models, the solution operates as a decentralized machine learning framework within the video surveillance infrastructure at the edge. Its primary focus is protecting private information extracted from captured video streaming data. This research integrates cutting-edge machine learning techniques, including scene graph generation and semantic communication approaches, by enabling edge nodes to exchange parameters for training, referencing, and safeguarding data privacy and ownership. These innovations collectively contribute to the protection of human privacy. The performance evaluation confirms that the solution is an efficient and effective privacy protection platform, offering a significant advancement over conventional centralized solutions.

With the increasing convenience of location-based services (LBSs), there have been growing concerns about the risk of privacy leakage. We show that existing techniques fail to defend against a statistical attack meant to infer the user’s location privacy and query privacy, which is due to continuous queries that the same user sends in the same location in a short time, causing the user’s real location to appear consecutively more than once and the query content to be the same or similar in the neighboring query. They also fail to consider the hierarchical structure of the address, so locations in an anonymous group may be located in the same organization, resulting in leaking of the user’s organization information and reducing the privacy protection effect. This paper presents a dummy generation scheme, considering the hierarchical structure of the address (DGS-HSA). In our scheme, we introduce a novel meshing method, which divides the historical location dataset according to the administrative region division. We also choose dummies from the historical location dataset with the two-level grid structure to realize the protection of the user’s location, organization information, and query privacy. Moreover, we prove the feasibility of the presented scheme by solving the multi-objective optimization problem and give the user’s privacy protection parameters recommendation settings, which balance the privacy protection level and system overhead. Finally, we evaluate the effectiveness and the correctness of the DGS-HSA through theoretical analysis and extensive simulations.

This research presents an effective way for protecting privacy in video surveillance data security. This technique integrates numerous critical challenges in order to generate a public stream and a private remnant erroneous the proposed approach distorted the protected, vulnerable portion. The proposed technique is built around the following three primary concerns: recognizing information that is cognizant of privacy, safeguarding anonymity, and maintaining confidential data through video encoding. In the first stage, the Privacy Region Mask Separation approach is used to identify the portion that is susceptible to protection. The sensitive part is located using this method. Then, the Adaptive Silhouette-Blur Guided Privacy Protection Scheme (ASBGPPS) is considered for privacy protection. An improved version of the Coati optimization algorithm called SBGPPS and ICOA are both incorporated into it.. Lastly, the Storage-H.264/AVC approach is considered in order to accomplish the video encoding and secret sharing storage. Finally, we have completed the video restoration. In order to evaluate how well the proposed methodology works in comparison to more traditional methods, performance measurements are utilized.

This paper addresses video surveillance of Chilean municipalities using drones as a low-cost policing response to urban insecurity and its impact on the right to privacy and personal data protection. The text explains the municipal police powers that have been invoked to uphold the legality of video surveillance at the local level. In examining four municipal cases, it looks at how surveillance is being carried out with drones and how this has been supervised by the competent authorities, concluding that there are real risks to privacy in the operation of surveillance systems. To this end, the issue is contrasted with the applicable regulatory standards and comparative solutions. Chile lacks specific regulations for video surveillance and a robust legal framework to protect personal data. The paper also evaluates the action of courts that have legitimized surveillance, even without a legal basis, concluding that the combination of low-cost and highly intrusive technologies and the lack of an adequate legal framework creates a significant threat to the exercise of privacy and personal data protection at municipal level.

In recent years, the security and privacy issues of face data in video surveillance have become one of the hotspots. How to protect privacy while maintaining the utility of monitored faces is a challenging problem. At present, most of the mainstream methods are suitable for maintaining data utility with respect to pre-defined criteria such as the structure similarity or shape of the face, which bears the criticism of poor versatility and adaptability. This paper proposes a novel generative framework called Quality Maintenance-Variational AutoEncoder (QM-VAE), which takes full advantage of existing privacy protection technologies. We innovatively add the loss of service quality to the loss function to ensure the generation of de-identified face images with guided quality preservation. The proposed model automatically adjusts the generated image according to the different service quality evaluators, so it is generic and efficient in different service scenarios, even some that have nothing to do with simple visual effects. We take facial expression recognition as an example to present experiments on the dataset CelebA to demonstrate the utility-preservation capabilities of QM-VAE. The experimental data show that QM-VAE has the highest quality retention rate of 86%. Compared with the existing method, QM-VAE generates de-identified face images with significantly improved utility and increases the effect by 6.7%.

The surveillance videos captured by cameras deployed in large-scale places can provide help for path planning of crowd evacuation in emergencies. However, most surveillance videos are not allowed to be shared in order to avoid privacy disclosure. Thus, it is difficult to obtain the video data of the whole scenario for path planning which greatly reduces effectiveness of path planning. How to provide global path planning for crowd evacuation while ensuring privacy protection is a challenging problem. To solve this problem, we propose a federated learning based path planning method for crowd evacuation. In this method, the potential field model are introduced into the federated learning framework to provide the privacy protection since only the potential field information is aggregated between the camera terminal and the central server, instead of the surveillance videos. First, we construct the local potential field for the surveillance scene of each camera by extracting the scene information and the crowds information. Second, the central server periodically performs the global potential field information aggregation and integrates these information to plan the optimal global path dynamically for crowd evacuation. Finally, we implemente a simulation platform to simulate the process of crowd movement and visualize the dynamic change of the potential energy field. Experimental results prove that the proposed method can provide privacy-preserving path planning for multi-camera evacuation scenarios.

Trajectory data often provides useful information that can be utilized in real-life applications, such as traffic planning and location-based advertising. Because people’s trajectory information can result in serious personal privacy leakage, trajectory privacy protection methods are employed. However, existing methods assume and use the same privacy requirements for all trajectories, which affect privacy protection efficiency and data utilization. This paper proposes a trajectory privacy protection method based on user requirement. By dividing different time intervals, it sets different privacy protection parameters for different trajectories to provide more detailed privacy protection. The proposed method utilizes the divided time intervals and privacy protection requirements to form a privacy requirement matrix, to construct an anonymous trajectory equivalence class and undirected graph. Then, trajectories are processed to form anonymous sets. Euclidean distance is also replaced with Manhattan distance in calculating the distance of the trajectories, which would improve the privacy protection and data utility and narrow the gap between the theoretical privacy protection and the actual protective effects. Comparative experiments demonstrate that the proposed method outperforms other similar methods in regards to both privacy protection and data utilization.

Location-based services (LBSs) are a crucial element of the Internet of Things (IoT) and have garnered significant attention from both researchers and users, driven by the rise of wireless devices and a growing user base. However, the use of LBS-enabled applications carries several risks, as users must provide their real locations with each query. This can expose them to potential attacks from the LBS server, leading to serious issues like the theft of personal information. Consequently, protecting location privacy is a vital concern. To address this, location dummy-based methods are employed to safeguard the location privacy of LBS users. However, location dummy-based approaches also suffer from problems such as low resistance against inference attacks and the generation of strong dummy locations, an issue that is considered an open problem. Moreover, generating many location dummies to achieve a high privacy protection level leads to high network overhead and requires high computational capabilities on the mobile devices of the LBS users, and such devices are limited. In this paper, we introduce the Caching-Aware Double-Dummy Selection (CaDDSL) algorithm to protect the location privacy of LBS users against homogeneity location and semantic location inference attacks, which may be applied by the LBS server as a malicious party. Then, we enhance the CaDDSL algorithm via encapsulation with agents to solve the tradeoff between generating many dummies and large network overhead by proposing the Cache-Aware Overhead-Aware Dummy Selection (CaOaDSL) algorithm. Compared to three well-known approaches, namely GridDummy, CirDummy, and Dest-Ex, our approach showed better performance in terms of communication cost, cache hit ratio, resistance against inference attacks, and network overhead.

Interactive 360° video technology has not been embraced for surveillance purposes despite its ability to eliminate blind spots, which is an important attribute of video surveillance. Further, privacy invasion due to video surveillance has a negative impact, and this urges for attention. Hence, the paper authors considered these two aspects and proposed a conceptual design framework with its rationale for privacy protection in use within the infrastructure of the interactive 360° video surveillance system. This conceptual integration framework takes into account the next essential factors: i) the utilization of the positive characteristics of 360° video to improve surveillance; ii) the protection of people's privacy; iii) the assistance needed in crime investigation and forensics; and iv) the ease and cost-effectiveness for deployment. These are factors of paramount significance for public safety and social order and they can be guaranteed with proactive approaches of design, based on the latest developments of Internet of Things technology and digital watermarking advancements.

AutoPrivacy: Automatic privacy protection and tagging suggestion for mobile social photo

Distributed Video Coding (DVC) is a new paradigm for video compression based on the information theoretical results of Slepian and Wolf (SW), and Wyner and Ziv (WZ). While conventional coding has a rigid complexity allocation as most of the complex tasks are performed at the encoder side, DVC enables a flexible complexity allocation between the encoder and the decoder. The most novel and interesting case is low complexity encoding and complex decoding, which is the opposite of conventional coding. While the latter is suitable for applications where the cost of the decoder is more critical than the encoder's one, DVC opens the door for a new range of applications where low complexity encoding is required and the decoder's complexity is not critical. This is interesting with the deployment of small and battery-powered multimedia mobile devices all around in our daily life. Further, since DVC operates as a reversed-complexity scheme when compared to conventional coding, DVC also enables the interesting scenario of low complexity encoding and decoding between two ends by transcoding between DVC and conventional coding. More specifically, low complexity encoding is possible by DVC at one end. Then, the resulting stream is decoded and conventionally re-encoded to enable low complexity decoding at the other end. Multiview video is attractive for a wide range of applications such as free viewpoint television, which is a system that allows viewing the scene from a viewpoint chosen by the viewer. Moreover, multiview can be beneficial for monitoring purposes in video surveillance. The increased use of multiview video systems is mainly due to the improvements in video technology and the reduced cost of cameras. While a multiview conventional codec will try to exploit the correlation among the different cameras at the encoder side, DVC allows for separate encoding of correlated video sources. Therefore, DVC requires no communication between the cameras in a multiview scenario. This is an advantage since communication is time consuming (i.e. more delay) and requires complex networking. Another appealing feature of DVC is the fact that it is based on a statistical framework. Moreover, DVC behaves as a natural joint source-channel coding solution. This results in an improved error resilience performance when compared to conventional coding. Further, DVC-based scalable codecs do not require a deterministic knowledge of the lower layers. In other words, the enhancement layers are completely independent from the base layer codec. This is called the codec-independent scalability feature, which offers a high flexibility in the way the various layers are distributed in a network. This thesis addresses the following topics: First, the theoretical foundations of DVC as well as the practical DVC scheme used in this research are presented. The potential applications for DVC are also outlined. DVC-based schemes use conventional coding to compress parts of the data, while the rest is compressed in a distributed fashion. Thus, different conventional codecs are studied in this research as they are compared in terms of compression efficiency for a rich set of sequences. This includes fine tuning the compression parameters such that the best performance is achieved for each codec. Further, DVC tools for improved Side Information (SI) and Error Concealment (EC) are introduced for monoview DVC using a partially decoded frame. The improved SI results in a significant gain in reconstruction quality for video with high activity and motion. This is done by re-estimating the erroneous motion vectors using the partially decoded frame to improve the SI quality. The latter is then used to enhance the reconstruction of the finally decoded frame. Further, the introduced spatio-temporal EC improves the quality of decoded video in the case of erroneously received packets, outperforming both spatial and temporal EC. Moreover, it also outperforms error-concealed conventional coding in different modes. Then, multiview DVC is studied in terms of SI generation, which differentiates it from the monoview case. More specifically, different multiview prediction techniques for SI generation are described and compared in terms of prediction quality, complexity and compression efficiency. Further, a technique for iterative multiview SI is introduced, where the final SI is used in an enhanced reconstruction process. The iterative SI outperforms the other SI generation techniques, especially for high motion video content. Finally, fusion techniques of temporal and inter-view side informations are introduced as well, which improves the performance of multiview DVC over monoview coding. DVC is also used to enable scalability for image and video coding. Since DVC is based on a statistical framework, the base and enhancement layers are completely independent, which is an interesting property called codec-independent scalability. Moreover, the introduced DVC scalable schemes show a good robustness to errors as the quality of decoded video steadily decreases with error rate increase. On the other hand, conventional coding exhibits a cliff effect as the performance drops dramatically after a certain error rate value. Further, the issue of privacy protection is addressed for DVC by transform domain scrambling, which is used to alter regions of interest in video such that the scene is still understood and privacy is preserved as well. The proposed scrambling techniques are shown to provide a good level of security without impairing the performance of the DVC scheme when compared to the one without scrambling. This is particularly attractive for video surveillance scenarios, which is one of the most promising applications for DVC. Finally, a practical DVC demonstrator built during this research is described, where the main requirements as well as the observed limitations are presented. Furthermore, it is defined in a setup being as close as possible to a complete real application scenario. This shows that it is actually possible to implement a complete end-to-end practical DVC system relying only on realistic assumptions. Even though DVC is inferior in terms of compression efficiency to the state of the art conventional coding for the moment, strengths of DVC reside in its good error resilience properties and the codec-independent scalability feature. Therefore, DVC offers promising possibilities for video compression with transmission over error-prone environments requirement as it significantly outperforms conventional coding in this case.

Drift error is a critical issue in video scrambling which is widely used to protect privacy in surveillance video. However, how to improve video coding efficiency while preventing drift error in video scrambling has not been covered in most studies. Proposed is a prediction restricted scheme for H.264/AVC privacy protected video scrambling. Experiments show that the proposed scheme dramatically raises coding efficiency.

A facile fabrication strategy constructed multilayer piezoresistive pressure sensor for intelligent recognition system towards privacy protection

At home and abroad, smart home, after more than ten years development is getting more and more widely welcomed. Smart home technology is developing rapidly, and with the emergence of IPv6, smart home based on IPv6 is becoming a trend. Meanwhile, security and privacy protection of smart home is receiving more attention. Although IPv6 is more secure than IPV4, there are still issues of privacy loss. In this paper, we focus on the security and privacy protection of smart home based on IPv6 and propose two different solutions respectively, that is, security and privacy protection based on trust as well as that based on strategy. These two solutions achieve security and privacy protection via model about trust and strategy mechanism respectively. On the basis of these two solutions, a framework of security and privacy protection system of smart home is designed. Through the system we designed, the security and privacy protection of smart home based on IPv6 is achieved.