ViewMap: Sharing Private In-Vehicle Dashcam Videos

Today, search for dashcam video evidences is conducted manually and its procedure does not guarantee privacy. In this paper, we motivate, design, and implement ViewMap, an automated public service system that enables sharing of private dashcam videos under anonymity. ViewMap takes a profile-based approach where each video is represented in a compact form called a view profile (VP), and the anonymized VPs are treated as entities for search, verification, and reward instead of their owners. ViewMap exploits the line-of-sight (LOS) properties of dedicated short-range communications (DSRC) such that each vehicle makes VP links with nearby ones that share the same sight while driving. ViewMap uses such LOS-based VP links to build a map of visibility around a given incident, and identifies VPs whose videos are worth reviewing. Original videos are never transmitted unless they are verified to be taken near the incident and anonymously solicited. ViewMap offers untraceable rewards for the provision of videos whose owners remain anonymous. We demonstrate the feasibility of ViewMap via field experiments on real roads using our DSRC testbeds and trace-driven simulations.We sincerely thank our shepherd Dr. Ranveer Chandra and the anonymous reviewers for their valuable feedback. This work was supported by Samsung Research Funding Center for Future Technology under Project Number SRFC-IT1402-01

Dashcam forensics : a preliminary analysis of 7 dashcam devices

Dashboard cameras (\dashcams") are becoming an important in-car accessory used to record audio and visual footage of car journeys. The audio/video footage produced by dashcams have become important items of evidence. This paper explores the problems related to the management and processing of dashcam evidence, and in particular, highlights challenges to the admissibility of evidence submitted online. The key contribution of this paper is to outline the results of an experiment which aimed to reveal the prevalence and provenance of artefacts created by the use of dashcams on the SD storage system of seven dashcam systems. The research describes the provenance of evidential artefacts relating to: the dashcam recording mode, GPS data, vehicular speed data, licence plate data, and temporal data which was found in at least six locations - namely: NMEA files, configuration/diagnostic files, EXIF metadata, directory structures, filename structures and imagery watermarks

Occupant Privacy Perception, Awareness, and Preferences in Smart Office Environments

Building management systems tout numerous benefits, such as energy efficiency and occupant comfort but rely on vast amounts of data from various sensors. Advancements in machine learning algorithms make it possible to extract personal information about occupants and their activities beyond the intended design of a non-intrusive sensor. However, occupants are not informed of data collection and possess different privacy preferences and thresholds for privacy loss. While privacy perceptions and preferences are most understood in smart homes, limited studies have evaluated these factors in smart office buildings, where there are more users and different privacy risks. To better understand occupants' perceptions and privacy preferences, we conducted twenty-four semi-structured interviews between April 2022 and May 2022 on occupants of a smart office building. We found that data modality features and personal features contribute to people's privacy preferences. The features of the collected modality define data modality features -- spatial, security, and temporal context. In contrast, personal features consist of one's awareness of data modality features and data inferences, definitions of privacy and security, and the available rewards and utility. Our proposed model of people's privacy preferences in smart office buildings helps design more effective measures to improve people's privacy

Crime Reporting Through Social Media: Potential Opportunities in Community Policing

The popularity and usage of social media over the years has increased. Due to this increase there has now been an influx of information shared on a global platform. This information that has been shared can be as superficial as daily activities, food eaten or as sensitive as graphic crimes committed. The Perceived Social Media Anonymity Effect is a concept that I am introducing and seek to explore. It is based on the premise that allows one to relinquish the fear of being in large crowds and speaking up when crimes have been committed while also being able to seek solitude among numbers on a social media platform where it appears easier to report and inform. This concept stems from the Bystander Effect. Darley & Latane (1968) states that the Bystander Effect refers to the phenomenon surrounding the passivity of onlookers’ willingness to help or intervene when faced with critical situations where others are being harmed. This study reviewed literature and high-profile social media exposure cases and analyzed the following questions: To what extent is there a nexus between non-reporting of crimes and reporting on social media? Furthermore, what are the perceived factors an individual reports that they take into account when determining whether to post or share videos of crimes on social media platforms and/or not reporting to police? To explore these issues, the public cases of Eric Garner, Laquan McDonald and Kenneka Jenkins were used to determine the impact of social media and its usage in a way of spreading information to the general public and at times used as a catalyst for social change. Information from a range of sources including local and national newspaper articles, media interviews, Chicago Police Department and the New York Police Department are synthesized and analyzed. This study concludes by reviewing implications and findings and recommendations for future study. The popularity and usage of social media over the years has increased. Due to this increase there has now been an influx of information shared on a global platform. This information that has been shared can be as superficial as daily activities, food eaten or as sensitive as graphic crimes committed. The Perceived Social Media Anonymity Effect is a concept that I am introducing and seek to explore. It is based on the premise that allows one to relinquish the fear of being in large crowds and speaking up when crimes have been committed while also being able to seek solitude among numbers on a social media platform where it appears easier to report and inform. This concept stems from the Bystander Effect. Darley & Latane (1968) states that the Bystander Effect refers to the phenomenon surrounding the passivity of onlookers’ willingness to help or intervene when faced with critical situations where others are being harmed. This study reviewed literature and high-profile social media exposure cases and analyzed the following questions: To what extent is there a nexus between non-reporting of crimes and reporting on social media? Furthermore, what are the perceived factors an individual reports that they take into account when determining whether to post or share videos of crimes on social media platforms and/or not reporting to police? To explore these issues, the public cases of Eric Garner, Laquan McDonald and Kenneka Jenkins were used to determine the impact of social media and its usage in a way of spreading information to the general public and at times used as a catalyst for social change. Information from a range of sources including local and national newspaper articles, media interviews, Chicago Police Department and the New York Police Department are synthesized and analyzed. This study concludes by reviewing implications and findings and recommendations for future study

Secure and privacy-respecting documentation for interactive manufacturing and quality assurance

The automated documentation of work steps is a requirement of many modern manufacturing processes. Especially when it comes to important procedures such as safety critical screw connections or weld seams, the correct and complete execution of certain manufacturing steps needs to be properly supervised, e.g., by capturing video snippets of the worker to be checked in hindsight. Without proper technical and organizational safeguards, such documentation data carries the potential for covert performance monitoring to the disadvantage of employees. Naïve documentation architectures interfere with data protection requirements, and thus cannot expect acceptance of employees. In this paper we outline use cases for automated documentation and describe an exemplary system architecture of a workflow recognition and documentation system. We derive privacy protection goals that we address with a suitable security architecture based on hybrid encryption, secret-sharing among multiple parties and remote attestation of the system to prevent manipulation. We finally contribute an outlook towards problems and possible solutions with regards to information that can leak through accessible metadata and with regard to more modular system architectures, where more sophisticated remote attestation approaches are needed to ensure the integrity of distributed components

Neighborhood Watch 2.0: Private Surveillance and the Internet of Things

The use of low-cost cameras and internet-connected sensors is sharply increasing among local law enforcement, businesses, and average Americans. While the motives behind adopting these devices may differ, this trend means more data about the events on Earth is rapidly being collected and aggregated each day. Current and future products, such as drones and self-driving cars, contain cameras and other embedded sensors used by private individuals in public settings. To function, these devices must passively collect information about other individuals who have not given the express consent that is commonly required when one is actively using an online service, such as email or social media. Generally, courts do not recognize a right to privacy once a person enters public spaces. However, the impending convergence of privately-owned sensors gathering information about the surrounding world creates a new frontier in which to consider private liberties, community engagement, and civic duties. This Article will analyze the legal and technological developments surrounding: (1) existing data sources used by local law enforcement; (2) corporate assistance with law enforcement investigations; and (3) volunteering of personal data to make communities safer. After weighing relative privacy interests, this Article will explain, under current laws, the utility of private data to make communities safer, while simultaneously advancing the goals of fiscal responsibility, government accountability, and community engagement

Estimation of Depth Maps from Monocular Images using Deep Neural Networks

Computer vision tasks have seen recent improvements thanks to the development of deep learning and high-end hardware. One of these tasks is depth perception, which involves extracting three-dimensional information from two-dimensional elements like images and constructing a depth map. This kind of information is useful in many domains such as autonomous vehicles or scene reconstruction for augmented and virtual reality. Hu-mans and some other animals achieve this by using binocular vision (vision from two images) and some algorithms have been developed to imitate this process. However, re-cent progress has enabled the advancement of other approaches that allow monocular vision algorithms to accomplish decent depth maps. In this thesis two monocular deep learning methods (Monodepth and DenseDepth) are explored and compared to each other (and with binocular and monocular approaches in general). This experiment is conducted by exposing the two methods to images that have not been seen during training and per-forming a qualitative analysis of their results in two different scenarios: indoors and out-doors. Both Monodepth and DenseDepth are able to produce depth maps, but DenseDepth results are more promising and reliable. Results show the importance of the training do-main, as the accuracy is affected by the choice of pre-trained models, as well as the col-lection and selection of data. It is still an open problem and seems unlikely that monocular depth perception could replace other sensors in critical systems like autonomous driving. However, it could still be a great complement or useful in other products or domains like photography.Doble Grado en Ingeniería Informática y Administración de Empresa