Releasing Search Queries and Clicks Privately Arne Bayer July 24, - PowerPoint PPT Presentation

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Search Queries and Clicks Privately Arne Bayer July 24, 2017 Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Table of Contents 1 A new Approach 2 Releasing Data Releasing Algorithm 3 Select-Queries q ∗ ∈ D 1 q ∗ / ∈ D 1 Arbitary d 4 Noisy Counts 5 Results Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Lists releasing anonymized lists tracing back possible Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Graphical Approach Let G ( E , V ) where: Vertices represent visited sites or search queries Edges represent links between sites Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results D 10 B 5 11 10 3 P 13 10 C 9 4 4 M 10 L Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Algorithm Parameters search log D Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Algorithm Parameters search log D noise parameters as scale parameter for Laplace distribution b , b c , b q Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Algorithm Parameters search log D noise parameters as scale parameter for Laplace distribution b , b c , b q b general noise b c noise on clicked URLs b q noise on queires Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Algorithm Parameters search log D noise parameters as scale parameter for Laplace distribution b , b c , b q b general noise b c noise on clicked URLs b q noise on queires d maximum queries kept per user Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Algorithm Parameters search log D noise parameters as scale parameter for Laplace distribution b , b c , b q b general noise b c noise on clicked URLs b q noise on queires d maximum queries kept per user d c maximum URL clicks kept per user Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Algorithm Parameters search log D noise parameters as scale parameter for Laplace distribution b , b c , b q b general noise b c noise on clicked URLs b q noise on queires d maximum queries kept per user d c maximum URL clicks kept per user M ( q , D ) = # of times q appeared in a given search log D Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Algorithm Parameters search log D noise parameters as scale parameter for Laplace distribution b , b c , b q b general noise b c noise on clicked URLs b q noise on queires d maximum queries kept per user d c maximum URL clicks kept per user M ( q , D ) = # of times q appeared in a given search log D K minimum threshold of occurences M ( q , D ) + Lap ( b ) > K Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Algorithm Parameters search log D noise parameters as scale parameter for Laplace distribution b , b c , b q b general noise b c noise on clicked URLs b q noise on queires d maximum queries kept per user d c maximum URL clicks kept per user M ( q , D ) = # of times q appeared in a given search log D K minimum threshold of occurences M ( q , D ) + Lap ( b ) > K K > d user limit smaller than threshold Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Algorithm A Algorithm Releasing Algorithm A 1: Input: D , d , d c , b , b q , b c , K Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Algorithm A Algorithm Releasing Algorithm A 1: Input: D , d , d c , b , b q , b c , K 2: Limit User Activity: Keep only d entries per user in D Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Algorithm A Algorithm Releasing Algorithm A 1: Input: D , d , d c , b , b q , b c , K 2: Limit User Activity: Keep only d entries per user in D 3: Count Queries calculate absolute commonness of all Queries q : M ( q , D ) Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Algorithm A Algorithm Releasing Algorithm A 1: Input: D , d , d c , b , b q , b c , K 2: Limit User Activity: Keep only d entries per user in D 3: Count Queries calculate absolute commonness of all Queries q : M ( q , D ) 4: Select-Queries: add all Queries to Q that exceed K : Q ← { q : M ( q , D ) + Lap ( b ) > K } Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Algorithm A Algorithm Releasing Algorithm A 1: Input: D , d , d c , b , b q , b c , K 2: Limit User Activity: Keep only d entries per user in D 3: Count Queries calculate absolute commonness of all Queries q : M ( q , D ) 4: Select-Queries: add all Queries to Q that exceed K : Q ← { q : M ( q , D ) + Lap ( b ) > K } add fuzziness to to Queries: 5: Get-Query-Counts: � q , M ( q , D ) + Lap ( b q ) � Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Algorithm A Algorithm Releasing Algorithm A 1: Input: D , d , d c , b , b q , b c , K 2: Limit User Activity: Keep only d entries per user in D 3: Count Queries calculate absolute commonness of all Queries q : M ( q , D ) 4: Select-Queries: add all Queries to Q that exceed K : Q ← { q : M ( q , D ) + Lap ( b ) > K } add fuzziness to to Queries: 5: Get-Query-Counts: � q , M ( q , D ) + Lap ( b q ) � 6: Get-Click-Counts: calculate top ten clicks and add fuzziness: � q , u , # u q + Lap ( b c ) � Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Privacy Guarantee � � � � A ( D 1 ) ∈ ˆ A ( D 2 ) ∈ ˆ Pr D ≤ α Pr D + δ 1 (1) Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Privacy Guarantee � � � � A ( D 1 ) ∈ ˆ A ( D 2 ) ∈ ˆ Pr D ≤ α Pr D + δ 1 (1) � � � � A ( D 2 ) ∈ ˆ A ( D 1 ) ∈ ˆ Pr D ≤ α Pr D + δ 1 (2) Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Privacy Guarantee � � � � A ( D 1 ) ∈ ˆ A ( D 2 ) ∈ ˆ Pr D ≤ α Pr D + δ 1 (1) � � � � A ( D 2 ) ∈ ˆ A ( D 1 ) ∈ ˆ Pr D ≤ α Pr D + δ 1 (2) ǫ alg = d · ln ( α ) + d / b q + d c / b c � d − K δ alg = d � 2 exp b � � e 1 / b , 1 + 1 with α = max 2 e ( K − 1) / b − 1 Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Steps to prove, that privacy is garanteed Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Steps to prove, that privacy is garanteed 4 Select-Queries Limit User: d = 1 Arbitary d Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Steps to prove, that privacy is garanteed 4 Select-Queries Limit User: d = 1 Arbitary d 5 Get-Query-Counts Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Releasing Algorithm Steps to prove, that privacy is garanteed 4 Select-Queries Limit User: d = 1 Arbitary d 5 Get-Query-Counts 6 Get-Click-Counts Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Part 1: d = 1 limit of 1 query per user Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Part 1: d = 1 limit of 1 query per user let D 1 and D 2 differ in exactly q ∗ Arne Bayer Releasing Search Queries and Clicks Privately

A new Approach Releasing Data Select-Queries Noisy Counts Results Part 1: d = 1 limit of 1 query per user let D 1 and D 2 differ in exactly q ∗ q ∗ ∈ D 1 and q ∗ ∈ D 2 Arne Bayer Releasing Search Queries and Clicks Privately