Differentially Private Safe Browsing: Aes-encrypted User Data For Real-time Phishing Detection

• Author(s):

  Ms.U.SathyaM.E | K.Athisri | A.Abinaya | A.Archana | G.K.Bavidra

• Keywords:

  AES Encryption, Differential Privacy, Dynamic Blacklisting, Machine Learning, Malicious URL Detection, Privacy-Preserving Safe Browsing (PPSB), Support Vector Machine (SVM)

• Abstract:

  With The Rise In Internet Usage, The Dangers Brought About By Malicious URLs And Phishing Websites Have Also Escalated, Thus The Need For Internet Security Becoming Paramount. Existing Solutions To Detect Malicious URL/website Include The Blacklisting Method And The Rule-based Technique, Which May Not Be Efficient In Detecting New Attacks. This Research Aims At Proposing A Privacy-Preserving Safe Browsing (PPSB) Scheme, Which Is An Amalgamation Of Machine Learning And Cryptographic Technology, With The Objective Of Increasing Both Security And Privacy Of The Users. The SVM Algorithm Is Used To Detect Malicious Websites Through Classification Based On The Features Extracted. In Addition To Detection Precision, The Suggested Framework Stresses Its Capability For Offering A High Level Of Privacy Guarantees With The Integration Of AES Encryption In Order To Protect User Searching Histories And Browsing Habits. In Such A Way, Sensitive Data Are Guaranteed To Be Safe From Any Third-party Analysts As Well As Service Operators. Moreover, The Framework Includes A Dynamic Blacklisting Function, Which Allows Users To Update And Manage The Blacklisted Websites On An Ongoing Basis. Finally, It Offers The Possibility Of Selectively Aggregating The Information About The Users' Activities While Providing Differential Privacy Guarantees.

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Other Details

• Paper id:

  IJSARTV12I5105318

• Published in:

  Volume: 12 Issue: 5 May 2026

• Publication Date:

  2026-05-10

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