Search in Social Networks with Access Control
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1 Search in Social Networks with Access Control Truls A. Bjørklund, Michaela Götz, Johannes Gehrke Norwegian University of Science and Technology, Cornell University 1 KEYS 2010, Michaela Götz
2 Content Search in Social Networks Where s the Chopin video? 2 KEYS 2010, Michaela Götz
3 Search System Desiderata Want a system that: Given a query (set of keywords) returns top-k most recent posts containing these keywords adheres to the privacy settings of users (can only retrieve friends posts) yiipppiiiii makes new posts immediately searchable Hatiku hampa tanpa cinta answers queries quickly does not consume too much space 3 KEYS 2010, Michaela Götz
4 Informal Problem Definition Given a social network Nodes = Users Edges indicate friendship (selflinks omitted) Given posts written by users (the authors) Answer conjunctive queries Result of a query Top-k most recent posts that 1. Contain all keywords of the query 2. Are authored by friends Queries with access control 5 4 KEYS 2010, Michaela Götz
5 Design Space Two axes of enforcing access control: Index axis: A group index contains the posts of a subset of users An index design is a set of group indexes Access axis: A group author list is a sorted list of pairs <post-id, author-id> for a subset of users An access design is a set of group author lists Intuition: Query processing 5 KEYS 2010, Michaela Götz
6 Examples - Index / Access Designs We distinguish designs based on: Index Design Access Design Cardinality # of indexes # of author lists Redundancy avg # of indexes a user is member of avg # of lists a user is member of 6 KEYS 2010, Michaela Götz
7 Examples Index Designs Global Index I1: No redundancy Lowest cardinality Friends Indexes: I1: I2: I3: I4: I5: High redundancy High cardinality 7 KEYS 2010, Michaela Götz
8 Examples Access Designs Global List L1: No redundancy Lowest cardinality Friends List: L1: L2: L3: L4: L5: High redundancy High cardinality 8 KEYS 2010, Michaela Götz
9 Terminology Covers: A set of group indexes covers a set of users if each user s posts are contained in at least one group index. Exact covers: no posts of other users. A set of group author lists covers a set of users if each user s posts are contained at least one group author list. Exact covers: no posts of other users. 9 KEYS 2010, Michaela Götz
10 Query Processing with Access Control Given query t 1,, t m by user u 1. Select indexes covering u s friends. 2. Select author lists covering u s friends. 3. Within each selected index: a) Intersect posting lists for t 1,, t m with the union of the selected author lists. b) For each result check whether u is friends with author. 4. Union the results of the indexes indexes friends (( posting lists for t 1,, t m ) ( author lists)) Processing Optimizations: Group indexes are exact cover no further filtering (skip 2., 3.) Group author lists are exact cover no friendship check (skip 3.b)) 10 KEYS 2010, Michaela Götz
11 Examples Index + Access Designs Friends Indexes / No Group Author lists: I1: I2: I3: I4: I5: High redundancy High cardinality Optimization: No author lists indexes friends (( posting lists for t 1,, t m ) ( author lists)) 11 KEYS 2010, Michaela Götz
12 Examples - Index + Access Designs User Indexes / No Group Author lists: I1: I2: I3: I4: I5: No redundancy High cardinality Optimization: No friends lookup indexes friends (( posting lists for t 1,, t m ) ( author lists)) 12 KEYS 2010, Michaela Götz
13 Examples Index + Access Designs Global index / Global list Index: I1: No redundancy Lowest cardinality 5 Author lists: L1: No redundancy Lowest cardinality indexes friends (( posting lists for t 1,, t m ) ( author lists)) 13 KEYS 2010, Michaela Götz
14 Examples Index + Access Designs Global index / User list Index: I1: No redundancy Lowest cardinality 5 Author lists: L1: L2: L3: L4: L5: No redundancy High cardinality Optimization: No friends look-up indexes friends (( posting lists for t 1,, t m ) ( author lists)) 14 KEYS 2010, Michaela Götz
15 Examples Index + Access Designs Global index / Friends list Index: I1: Author lists: L1: L2: L3: L4: L5: No redundancy Lowest cardinality High redundancy High cardinality Optimization: No friends look-up indexes friends (( posting lists for t 1,, t m ) ( author lists)) 15 KEYS 2010, Michaela Götz
16 Implementation - Overview Main memory system in Java Updates: Small updatable index to add new posts hierarchy of indexes based on geometric partitioning with compression Operators over lists: Operators: Union, Intersection, Filter Methods: Next(), SkipTo(value v) 16 KEYS 2010, Michaela Götz
17 Experiments Comparison of the performance Across index designs Across access designs Across different social networks Performance measures: Time to answer query Time to add post Space consumption 17 KEYS 2010, Michaela Götz
18 Experiments Data: Network: real twitter network: 417,000 users synthetic networks (Barabasi s attachment model) varying size and degree Posts obtained from twitter Queries generated through a random process Run 100,000 queries returning top-100 posts Environment: 3.2GHz, 16GB RAM, Red Hat Enterprise KEYS 2010, Michaela Götz
19 Scalability Global / Global Friends / None User / None Global / None -> no access control Varying the number of documents fix 1,000 users, 20 friends per user Processing time (s) Update Cost cost (s) Search Search cost (s) Space consumption (MB) Number of documents 10 5 Processing time (s) Number of documents 10 5 Size (MB) 10 3 Number of documents 10 5 Number of posts x 10 5 Number of posts x 10 5 Number of posts x KEYS 2010, Michaela Götz
20 Index Design Performance under Varying Networks Global index / Global User indexes / None No Global access / None control Processing time (s) Processing time (s) Update Cost (s) Search cost (s) Space (MB) Varying the number of users Update cost (100 friends per user) Number of users Update cost (10,000 users) Number of friends per user Processing time (s) Processing time (s) Search cost (100 friends per user) Number of users Search cost (10,000 users) Number of friends per user fix 100 friends per user, 1mm posts Size (MB) Size (MB) Space consumption (100 friends per user) Number of users Varying the number of friends per user fix 10,00 users, 1mm posts Space consumption (10,000 users) Number of friends per user G U No 20 KEYS 2010, Michaela Götz
21 Access Design Performance under Varying Networks Global index / Global User Global indexes / Friends No Global access / User control Global / None Update Cost (s) Search cost (s) Space (MB) Varying the number of users Processing time (s) Processing time (s) Update cost (100 friends per user) Number of users in network Update cost (100,000 users) Search cost (100 friends per user) Number of users in network Search cost (100,000 users) Number of friendships per user Number of friendships per user fix 100 friends per user, 2.5mm posts Size (MB) Size (MB) Space consumption (100 friends per user) Number of users Varying the number of friends per user fix 100,00 users, 2.5mm posts Space consumption (100,000 users) Number of friends per user N 21 KEYS 2010, Michaela Götz
22 Experiment on Real Twitter Network Access designs with global index on real network Update Cost (s) Search cost (s) Space (MB) Global Global-list / Global User-lists / 10 2 Global Friends-lists / 10 1 No Global access / None control 22 KEYS 2010, Michaela Götz
23 Conclusions Two axis design space for access control in search: Index Axis Access Axis Experiments with five designs: Access designs reveal tradeoffs between index size, update and search performance Global Index / Friends lists fast searches (independent on network) slow updates (dependent on network) Global Index / User lists or Global Index / Global list slow searches (dependent on network) fast updates (independent on network) Similar tradeoffs for index designs Recommendation: Choose between user indexes and the global index with user or friends lists based on workload and network 23 KEYS 2010, Michaela Götz
24 Future Work Explore design space Identify best design for a particular workload and network Dynamic design Adapt to changes in the workload Adapt to changes in network Distribute system Extend to more advanced ranking functions Include network structure and interactions as features Do not leak private information through ranking 24 KEYS 2010, Michaela Götz
25 Thank You! Questions? 25 KEYS 2010, Michaela Götz
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