Password Management Strategies for Online Accounts Shirley Gaw

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Password Management Strategies for Online Accounts Shirley Gaw, Edward W. Felten Princeton University

Password Management Strategies for Online Accounts Shirley Gaw, Edward W. Felten Princeton University

Abstract Average number of unique passwords 3. 31 (n = 49, SD = 1.

Abstract Average number of unique passwords 3. 31 (n = 49, SD = 1. 76) …and average reuse 3. 18 (SD = 2. 71) People will reuse passwords more as they acquire more accounts

Abstract (continued) Why reuse? The reused ones were easier to remember People rely on

Abstract (continued) Why reuse? The reused ones were easier to remember People rely on their memory rather than store passwords

Abstract (continued) Friends have the greatest ability to attack passwords Participants ranked those closest

Abstract (continued) Friends have the greatest ability to attack passwords Participants ranked those closest to them as having the greatest ability to compromise their passwords

Abstract (continued) Knowing personal information about a victim was seen as advantageous People worry

Abstract (continued) Knowing personal information about a victim was seen as advantageous People worry more about human guessing than automated guessing tools

Outline People will. Password reuse passwords Reuse more as they acquire more accounts People

Outline People will. Password reuse passwords Reuse more as they acquire more accounts People rely on their memory rather Reasons Reuse than storefor passwords Participants ranked those closest to Perceptions Attackers them as having theof greatest ability to compromise their passwords People worry more about human Perceptions Attacks guessingofthan automated guessing tools

Participants 18 16 58 49 40 33

Participants 18 16 58 49 40 33

Outline • Password Reuse • Reasons for Reuse • Perceptions of Attackers • Perceptions

Outline • Password Reuse • Reasons for Reuse • Perceptions of Attackers • Perceptions of Attack

Password Reuse: Method First Pass: (n = 49) • Select from 139 websites •

Password Reuse: Method First Pass: (n = 49) • Select from 139 websites • Login to each website • Self-report summary statistics Second Pass: • List other websites used personally • Re-report summary statistics

Password Reuse: Results Unique passwords M = 3. 31, SD = 1. 76 (n

Password Reuse: Results Unique passwords M = 3. 31, SD = 1. 76 (n = 49) Passwords reuse rate M = 3. 18, SD = 2. 71

Password Reuse: Results People will reuse passwords more as they acquire more accounts

Password Reuse: Results People will reuse passwords more as they acquire more accounts

Outline • Password Reuse • Reasons for Reuse • Perceptions of Attackers • Perceptions

Outline • Password Reuse • Reasons for Reuse • Perceptions of Attackers • Perceptions of Attack

Reasons for Reuse: Method 115 question survey (n = 58) • Demographic information •

Reasons for Reuse: Method 115 question survey (n = 58) • Demographic information • Explanations of password reuse/avoidance • Descriptions of password creation/storage • Descriptions of password management

Reasons for Reuse: Results Why use a different password? I don’t • Security (12)

Reasons for Reuse: Results Why use a different password? I don’t • Security (12) like to think that if someone has access to one of • Website has credit card, etc (11) my passwords, she or he could • Website restricts password format (10) access all of my information • Website is important (7) for all of the pages I log into. • Website is in a particular category (4) • Other (12)

Reasons for Reuse: Results Why use the same password? It is easier to remember

Reasons for Reuse: Results Why use the same password? It is easier to remember (35)

Reasons for Reuse: Results Why use the same password? It is easier to remember

Reasons for Reuse: Results Why use the same password? It is easier to remember (35) People rely on their memory rather than store passwords

Outline • Password Reuse • Reasons for Reuse • Perceptions of Attackers • Perceptions

Outline • Password Reuse • Reasons for Reuse • Perceptions of Attackers • Perceptions of Attack

Perceptions of Attackers: Method • Who could compromise password? Rank – Ability – Motivation

Perceptions of Attackers: Method • Who could compromise password? Rank – Ability – Motivation – Likelihood • Categories of people – – – Friend Acquaintance (tech & non-tech) Competitor Insider Hacker (n = 56)

(n = 56) Most Able Attackers

(n = 56) Most Able Attackers

(n = 54) Least Able Attackers

(n = 54) Least Able Attackers

(n = 56) Most Motivated Attackers

(n = 56) Most Motivated Attackers

(n = 56) Least Motivated Attackers

(n = 56) Least Motivated Attackers

(n = 56) Most Likely Attackers

(n = 56) Most Likely Attackers

(n = 55) Least Likely Attackers

(n = 55) Least Likely Attackers

Likely attackers: Motivated or Able? • Logit regression on ranking responses* • Odds on

Likely attackers: Motivated or Able? • Logit regression on ranking responses* • Odds on ranking someone as likely – Motivation: 6. 28 x – Ability: 3. 82 x *Thanks to Pierre-Antoine Kremp

Perceptions of Attackers: Results Participants ranked those closest to them as having the greatest

Perceptions of Attackers: Results Participants ranked those closest to them as having the greatest ability to compromise their passwords

Outline • Password Reuse • Reasons for Reuse • Perceptions of Attackers • Perceptions

Outline • Password Reuse • Reasons for Reuse • Perceptions of Attackers • Perceptions of Attack

Perceptions of Attacks: Method Given: (n = 56) 13 tips for creating strong passwords

Perceptions of Attacks: Method Given: (n = 56) 13 tips for creating strong passwords – 3 passwords – Password construction method Task: • Rank passwords by strength • Explain ranking

Perceptions of Attacks: Results Princeton. NJ is too easy for someone to guess if

Perceptions of Attacks: Results Princeton. NJ is too easy for someone to guess if they know where you live One would have to know her decently well to know her favorite novel

Perceptions of Attacks: Results People worry more about human guessing than automated guessing tools

Perceptions of Attacks: Results People worry more about human guessing than automated guessing tools

Good News / Bad News • Good news: Participants understood the threat posed by

Good News / Bad News • Good news: Participants understood the threat posed by those closest to them • Bad news: They didn’t understand the threat of dictionary attacks

Good News / Bad News • Good news: Participants were concerned about the weakness

Good News / Bad News • Good news: Participants were concerned about the weakness of poor passwords • Good news: They relied on their memory rather than poorly secured storage (ie. , paper) • Bad news: They feel and act as if they do not have any better tools or strategies

Good News / Bad News • Good news: Participants had few accounts with password

Good News / Bad News • Good news: Participants had few accounts with password authentication • Bad news: They had even fewer passwords

Outline • Password Reuse • Reasons for Reuse • Perceptions of Attackers • Perceptions

Outline • Password Reuse • Reasons for Reuse • Perceptions of Attackers • Perceptions of Attack