Net Trust: User-Centered Detection of Pharming, Phishing and Fraud. L Jean Camp
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1 Net Trust: User-Centered Detection of Pharming, Phishing and Fraud L Jean Camp
2 Core Problem Statement How to inform individual assessments of trustworthiness of a potential online transaction.
3 Design for Trust Start with human trust behaviors Trust Used for simplification Encompasses discrete technical problems privacy, integrity, data security Embeds discrete policy problems business behavior, customer service, quality of goods, privacy
4 Human vs. Computer Trust Computers Process data Store data Transmit data Distinguish atomicity, privacy, availability, Humans Understand context Evaluate uncertainty Make lumping decisions based on context Begin with the human as the basis of the design Examine human interactions Signal humans using pre-existing social capital
5 Net Trust Goals Detect fraud Notification Warning the user Prevention Refuse to connect or require coping the url Remediation Connection to a remediation service
6 Trust and Context vs. Resource Verification Resources are often fairly easy to identify as good or bad in physical realms
7 Trust and Context Identity Verification
8 Current Signaling Seals Traditional mechanisms to communicate trustworthiness.
9 Signaling Requires Malicious Party to Cooperate? =
10 Social Ratings Don t Depend on Third Parties Bob Alice is friend s with Bob, and trusts Bob s opinion Bob likes this website Alice Alice has never heard of this website, but will inform her trust decision with Bob s experience with website Website
11 Net Trust Reputations Do not require explicit user action, but are created by observations of user behaviors. Variables underlying the ratings are neither under control of attackers nor subject to highly parallel attacks. The ratings integrate personal, social network, and centralized information sources. The identity of the participants in a social network used for ratings are known only to members of that social network
12 Done & Working Ratings Engine Implicit ratings (history-based) Explicit ratings (manual interaction), comments Local evaluation with age threshold adjustment Toolbar UI Correct updates; coherent over tabs & windows Social Network Manual invitation and buddy ID entry Self-enforcement of rating partition over personas Synchronization Local ratings storage Immediate server read/write on persona load/unload
13 Views
14 Security & Privacy Properties Sybil attack resistance Web scripting resistance Server authentication (anti-spoofing) Write authentication for peer records NT ID to address commitment NT ID deniability ( That s not my ID ) Linking resistance (NT ID and personal info) Social network confidentiality
15 Short Term Objectives Synchronization (protecting social network) Time delays for server access on persona change Anonymous server access via Tor Third-Party rating assurance Net Trust Certificate Authority Signed rating lists Social Network Mandatory history partition over multiple personas Invite automation & validation
16 Longer Term Initiatives Expand rating sets for client-side pharming detection Include server IP address & certs in history Blend rating sets across social networks Deter unauthorized sharing of NT IDs Improves ID deniability Improves information diffusion Enable server intersection attack on social network Narrative risk communication Rich warnings: cartoons, video, animation
17 Architectural Overview Peer Client Rating Engine Toolbar UI Synchronization Social Network File System Peer Invitations Other Peer Clients Server CGI Web End Peer Ratings Store Third-Party Store Third-Party Rating Producers
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