2020 Identity Theft Statistics | Consumeraffairs
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Cyber Threats to Mobile Phones Paul Ruggiero and Jon Foote
Cyber Threats to Mobile Phones Paul Ruggiero and Jon Foote Mobile Threats Are Increasing Smartphones, or mobile phones with advanced capabilities like those of personal computers (PCs), are appearing in more people’s pockets, purses, and briefcases. Smartphones’ popularity and relatively lax security have made them attractive targets for attackers. According to a report published earlier this year, smartphones recently outsold PCs for the first time, and attackers have been exploiting this expanding market by using old techniques along with new ones.1 One example is this year’s Valentine’s Day attack, in which attackers distributed a mobile picture- sharing application that secretly sent premium-rate text messages from the user’s mobile phone. One study found that, from 2009 to 2010, the number of new vulnerabilities in mobile operating systems jumped 42 percent.2 The number and sophistication of attacks on mobile phones is increasing, and countermeasures are slow to catch up. Smartphones and personal digital assistants (PDAs) give users mobile access to email, the internet, GPS navigation, and many other applications. However, smartphone security has not kept pace with traditional computer security. Technical security measures, such as firewalls, antivirus, and encryption, are uncommon on mobile phones, and mobile phone operating systems are not updated as frequently as those on personal computers.3 Mobile social networking applications sometimes lack the detailed privacy controls of their PC counterparts. Unfortunately, many smartphone users do not recognize these security shortcomings. Many users fail to enable the security software that comes with their phones, and they believe that surfing the internet on their phones is as safe as or safer than surfing on their computers.4 Meanwhile, mobile phones are becoming more and more valuable as targets for attack. -
Suspect Until Proven Guilty, a Problematization of State Dossier Systems Via Two Case Studies: the United States and China
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations Fall 2009 Suspect Until Proven Guilty, a Problematization of State Dossier Systems via Two Case Studies: The United States and China Kenneth N. Farrall University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Asian Studies Commons, Communication Technology and New Media Commons, International and Intercultural Communication Commons, and the Social Influence and oliticalP Communication Commons Recommended Citation Farrall, Kenneth N., "Suspect Until Proven Guilty, a Problematization of State Dossier Systems via Two Case Studies: The United States and China" (2009). Publicly Accessible Penn Dissertations. 51. https://repository.upenn.edu/edissertations/51 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/51 For more information, please contact [email protected]. Suspect Until Proven Guilty, a Problematization of State Dossier Systems via Two Case Studies: The United States and China Abstract This dissertation problematizes the "state dossier system" (SDS): the production and accumulation of personal information on citizen subjects exceeding the reasonable bounds of risk management. SDS - comprising interconnecting subsystems of records and identification - damage individual autonomy and self-determination, impacting not only human rights, but also the viability of the social system. The research, a hybrid of case-study and cross-national comparison, was guided in part by a theoretical model of four primary SDS driving forces: technology, political economy, law and public sentiment. Data sources included government documents, academic texts, investigative journalism, NGO reports and industry white papers. The primary analytical instrument was the juxtaposition of two individual cases: the U.S. -
We Are All Rwandans”
UNIVERSITY OF CALIFORNIA Los Angeles “We are all Rwandans”: Imagining the Post-Genocidal Nation Across Media A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Film and Television by Andrew Phillip Young 2016 ABSTRACT OF DISSERTATION “We are all Rwandans”: Imagining the Post-Genocidal Nation Across Media by Andrew Phillip Young Doctor of Philosophy in Film and Television University of California, Los Angeles, 2016 Professor Chon A. Noriega, Chair There is little doubt of the fundamental impact of the 1994 Rwanda genocide on the country's social structure and cultural production, but the form that these changes have taken remains ignored by contemporary media scholars. Since this time, the need to identify the the particular industrial structure, political economy, and discursive slant of Rwandan “post- genocidal” media has become vital. The Rwandan government has gone to great lengths to construct and promote reconciliatory discourse to maintain order over a country divided along ethnic lines. Such a task, though, relies on far more than the simple state control of media message systems (particularly in the current period of media deregulation). Instead, it requires a more complex engagement with issues of self-censorship, speech law, public/private industrial regulation, national/transnational production/consumption paradigms, and post-traumatic media theory. This project examines the interrelationships between radio, television, newspapers, the ii Internet, and film in the contemporary Rwandan mediascape (which all merge through their relationships with governmental, regulatory, and funding agencies, such as the Rwanda Media High Council - RMHC) to investigate how they endorse national reconciliatory discourse. -
The Scoring of America: How Secret Consumer Scores Threaten Your Privacy and Your Future
World Privacy Forum The Scoring of America: How Secret Consumer Scores Threaten Your Privacy and Your Future By Pam Dixon and Robert Gellman April 2, 2014 Brief Summary of Report This report highlights the unexpected problems that arise from new types of predictive consumer scoring, which this report terms consumer scoring. Largely unregulated either by the Fair Credit Reporting Act or the Equal Credit Opportunity Act, new consumer scores use thousands of pieces of information about consumers’ pasts to predict how they will behave in the future. Issues of secrecy, fairness of underlying factors, use of consumer information such as race and ethnicity in predictive scores, accuracy, and the uptake in both use and ubiquity of these scores are key areas of focus. The report includes a roster of the types of consumer data used in predictive consumer scores today, as well as a roster of the consumer scores such as health risk scores, consumer prominence scores, identity and fraud scores, summarized credit statistics, among others. The report reviews the history of the credit score – which was secret for decades until legislation mandated consumer access -- and urges close examination of new consumer scores for fairness and transparency in their factors, methods, and accessibility to consumers. About the Authors Pam Dixon is the founder and Executive Director of the World Privacy Forum. She is the author of eight books, hundreds of articles, and numerous privacy studies, including her landmark Medical Identity Theft study and One Way Mirror Society study. She has testified before Congress on consumer privacy issues as well as before federal agencies. -
Address Munging: the Practice of Disguising, Or Munging, an E-Mail Address to Prevent It Being Automatically Collected and Used
Address Munging: the practice of disguising, or munging, an e-mail address to prevent it being automatically collected and used as a target for people and organizations that send unsolicited bulk e-mail address. Adware: or advertising-supported software is any software package which automatically plays, displays, or downloads advertising material to a computer after the software is installed on it or while the application is being used. Some types of adware are also spyware and can be classified as privacy-invasive software. Adware is software designed to force pre-chosen ads to display on your system. Some adware is designed to be malicious and will pop up ads with such speed and frequency that they seem to be taking over everything, slowing down your system and tying up all of your system resources. When adware is coupled with spyware, it can be a frustrating ride, to say the least. Backdoor: in a computer system (or cryptosystem or algorithm) is a method of bypassing normal authentication, securing remote access to a computer, obtaining access to plaintext, and so on, while attempting to remain undetected. The backdoor may take the form of an installed program (e.g., Back Orifice), or could be a modification to an existing program or hardware device. A back door is a point of entry that circumvents normal security and can be used by a cracker to access a network or computer system. Usually back doors are created by system developers as shortcuts to speed access through security during the development stage and then are overlooked and never properly removed during final implementation. -
SERI Conference Proceedings SHORTLISTED PAPERS
National Centre of Excellence for Cybersecurity Technology PRESENTS AT Development & Entrepreneurship OUR PARTNERS Promoting Cybersecurity Education, Research and Innovation SERI Conference Proceedings SHORTLISTED PAPERS THEME AUTHORS INSTITUTE/ ORGANIZATION Data Privacy Signify North America Considerations during HARSHA BANAVARA Corporation, Burlington, Requirements Phase of MA USA IoT Product Development ASTRA: A Post AKSHAY JAIN Exploitation Red Teaming DR. BHUVNA J Jain University, Bengaluru tool SUBARNA PANDA zSpaze Technologies, New Space and New RAMESH KUMAR V Bengaluru Threats PRASANNA PHANINDRAN Easwari Engineering College, Chennai Cross-Channel Scripting Attacks (XCS) in Web SHASHIDHAR R Bennett University Applications Quantum Cryptography SANCHALI KSHIRSAGAR UMIT SNDT Women's and Use Cases: A Short DR. SANJYA PAWAR University, Mumbai Survey Paper SHRAVANI SHAHAPURE NCoE-DSCI, New Delhi Securing IoT using Tata Advanced System SHALINI DHULL Permissioned Blockchain Limited, Noida Parul University, Vadodara An Analysis of Internet of YASSIR FAROOQUI Sankalchand Patel University, Things(IoT) Architecture DR. KIRIT MODI Visnagar Zonal advisory at Consumer Cyber Security-Modern Era DR. SUMANTA BHATTACHARYA Challenge to Human Race Rights Organization BHAVNEET KAUR SACHDEV and it’s impact on COVID-19 Indian Institute of Human Data Privacy Considerations during Requirements Phase of IoT Product Development HARSHA BANAVARA Data Privacy Considerations during Requirements Phase of IoT Product Development Harsha Banavara Signify North America -
Social Engineering Exposures
Aon Risk Solutions Financial Services Group Client Alert: Social Engineering Exposures While social engineering can refer to many things outside of a risk management context, within the insurance world, social engineering has assumed a specific, and notable, meaning. In brief, social engineering refers to the use of identity deception to gain the confidence of an employee to induce him or her to part with an organization’s money or securities. We will explore some of the most common forms of social engineering, relevant coverage interpretations, and available coverage solutions. Where Does The Threat Arise From? While a “theft” has undoubtedly occurred in a We’re here to Social engineering attacks can come in many social engineering loss, the commercial crime empower results forms, including: policy likely has not been triggered because the If you have questions employee did not “steal” the money/securities about your specific § Phishing scams are attempts by fraudsters to coverage or want more directly, and the employee certainly did not induce individuals into providing personal information, please benefit from it. Historically, crime policies did not information such as bank account numbers, contact your Aon broker. explicitly address social engineering, thus resulting passwords, and credit card numbers aon.com in varying outcomes. § Spear phishing is an electronic communications scam targeted towards a specific individual, Relevant Cases organization, or business Although a few courts have found coverage for § Vishing, or voice phishing is the criminal social engineering losses under crime policies, practice of using social engineering over the most courts have found that social engineering telephone to gain access to private personal and losses are not covered under the policy’s financial information Computer Fraud, Funds Transfer Fraud, or Forgery § SMiShing (short for “SMS phishing”) is a coverage agreements. -
Using Big Data Analytics to Fight Identity Fraud
Primary IDA Color Palette Secondary Color Palette - Medium R122 R247 R0 R0 R154 R250 R47 R165 G134 G143 G53 G114 G155 G187 G163 G194 B142 B30 B95 B156 B157 B119 B255 B218 Gray Orange Navy Blue Blue Using Big Data Analytics Gray Orange Navy Blue Blue R106 R84 R102 R191 R42 R147 R165 R216 G154 G148 G49 G191 to Fight Identity Fraud G197 G201 G113 G216 B194 B63 B144 B191 B255 B129 B206 B216 Light Blue Green Purple Light Gray Light Blue Green Purple Light Gray Dr. Stephen Coggeshall Chief Analytics and Science Officer ID Analytics/Lifelock 2nd Workshop of Mission-Critical Big Data Analytics Prairie View, May 16,17 A Symantec Company © 2017 ID Analytics, Inc. Confidential Primary IDA Color Palette Secondary Color Palette - Medium R122 R247 R0 R0 Outline R154 R250 R47 R165 G134 G143 G53 G114 G155 G187 G163 G194 B142 B30 B95 B156 B157 B119 B255 B218 Gray Orange Navy Blue Blue Gray Orange Navy Blue Blue • All about identity fraud R106 R84 R102 R191 – modes R42 R147 R165 R216 G154 G148 G49 G191 G197 G201 G113 G216 B194 B63 B144 B191 B255 B129 B206 B216 – examples Light Blue Green Purple Light Gray Light Blue Green Purple Light Gray • Using big data analytics to catch identity fraud – a look into our technology © 2017 ID Analytics, Inc. Confidential 2 One column Primary IDA Color Palette Secondary Color Palette - Medium ID Analytics Overview R122 R247 R0 R0 R154 R250 R47 R165 G134 G143 G53 G114 G155 G187 G163 G194 B142 B30 B95 B156 ID Analytics applies advanced analytics to a unique sources of U.S. -
The First Paper Presents the Results of a Joint Study Undertaken by Staff At
NONBANKS IN THE PAYMENTS SYSTEM: INNOVATION, COMPETITION, AND RISK— A Conference Summary By Richard J. Sullivan and Zhu Wang∗ From the early days of automated card sorting to the more recent times of the Internet and check imaging, payments and payments processing have continually embraced new technology. At the same time, the industry has been shaped by its share of entry and exit, through startups, mergers, and the reorganization of businesses seeking the proper scope of horizontal and vertical integration. Many of these changes have introduced new risks to payments. In response, public policy has evolved to help manage these risks. These changes have enabled nonbank organizations to play a larger role in the payments system. Nonbanks have followed a number of pathways to more prominence: purchasing bank payment processing subsidiaries, carving out niches in the payments market through innovation, and taking advantage of economies of scale made possible by shifting to electronic forms of payment. The contributions of nonbanks are undeniable. They have introduced some of the most far-reaching innovations to the payments system in recent years, leading to greater efficiencies in payments processing. At the same time, nonbanks have changed the dynamics of competition in payments, leading to a significant change in the system’s risk profile. The Federal Reserve Bank of Kansas City sponsored a conference on nonbanks in the payments system in Santa Fe, N.M., on May 2-4, 2007. The conference addressed many of the key questions raised by the growing presence of nonbanks in payments. Have recent payments innovations been more likely to come from nonbanks? Have nonbanks improved or harmed competition in payments? Have nonbanks increased risk or helped to develop tools to manage it? ∗ Richard J. -
Types of Fraud
Types of Fraud Phishing A form of identity theft in which a scammer uses an authentic-looking e-mail to trick recipients into giving out sensitive personal information, such as a credit card numbers, bank account numbers, Social Security numbers or other sensitive personal information. Vishing Voice phishing is the criminal practice of using social engineering over the telephone system, most often using features facilitated by Voice over IP (VoIP), to gain access to private personal and financial information from the public for the purpose of financial reward. The term is a combination of "voice" and phishing. Voice phishing exploits the public's trust in landline telephone services, which have traditionally terminated in physical locations known to the telephone company, and associated with a bill-payer. The victim is often unaware that VoIP makes formerly difficult-to-abuse tools/features of caller ID spoofing, complex automated systems (IVR), low cost, and anonymity for the bill-payer widely available. Voice phishing is typically used to steal credit card numbers or other information used in identity theft schemes from individuals. SMiShing In computing, SMS phishing or smishing is a form of criminal activity using social engineering techniques. Phishing is the act of attempting to acquire personal information such as passwords and credit card details by masquerading as a trustworthy entity in an electronic communication. SMS (Short Message Service) is the technology used for text messages on cell phones. SMS phishing uses cell phone text messages to deliver the bait to induce people to divulge their personal information. The hook (the method used to actually capture people's information) in the text message may be a website URL, but it has become more common to see a telephone number that connects to an automated voice response system. -
Member Guide
Table of Contents Welcome....................................................................................................................... 1 Consumer Services Student Loan Solutions ................................................................................................ 3 Credit Monitoring .......................................................................................................... 4 ID Theft & Fraud Resolution ........................................................................................ 5 Legal Advice ................................................................................................................. 6 Financial Advice............................................................................................................ 6 Online Legal Resource Center ..................................................................................... 7 Tax Preparation/Consultation ....................................................................................... 9 Terms & Conditions................................................................................................ 10-11 Phone Directory .......................................................................................................... 12 1 Welcome CONGRATULATIONS! Thank you for selecting the Student Assist Plus benefits plan and joining the thousands of other individuals that rely on our professional team to help them save money. Our valuable membership plan provides exclusive offers & savings on services such as: • Student -
The Onpoint Guide to Personal Cybersecurity
2021 GUIDE TO Personal cybersecurity Introduction: About this eBook . .3 Chapter 1: Securing your accounts . 4 Protect yourself from cybercrime . 5 Setting a strong username and password . 6 Chapter 2: Protecting your data online . .8 Social media cybersecurity best practices . 9 Malware and viruses . 10 Stay vigilant and protect your data . 11 Chapter 3: Digital protection checklist . 13 Chapter 4: How to spot and avoid common internet scams . .14 Digital account hijacking . 15 Sweetheart fraud . 16 Phishing, vishing, smishing . 17 Table of contents Quick money, unexpected windfalls, and too-good-to-be-true offers . 18 Fake approval notices . 19 Chapter 5: How to spot and avoid common offline scams . 20 ATM fraud . 21 SIM card swapping . 22 Chapter 6: Know the common signs of identity theft . 23. Chapter 7: What to do if you’ve been scammed . 24 Chapter 8: Tips for talking to your children about online safety . 25 Teaching kids about cybersecurity . 26 Chapter 9: Small business cybersecurity . .27 Chapter 10: List of resources . 28 Conclusion . 29 2 The risk of identity theft has grown in large part due to the rising number of connected devices in our lives. Every time we open an account to try a new service, swipe our cards, or participate in social media, we increase the risk of exposure. Millions of Americans are the victims of identity theft and fraud each year. With massive data breaches frequently making headlines, even the most careful consumers are open to attack. Oregon and Washington ranked 33rd and 22nd in identity theft complaints in 2019.1 Nationally, U.S.