Introducing Access Control in Webdamlog Serge Abiteboul INRIA

Introducing Access Control in Webdamlog Serge Abiteboul INRIA Saclay & ENS Cachan Joint work with Emilien Antoine, Gerome Miklau, Julia Stoyanovich and Vera Zaychik Moffitt Mai 30, 2012 ICDE 2012

• • • The Web as a distributed knowledge base Webdamlog: a rule-based language for the Web Access control in Webdamlog The Webdamlog system Conclusion Abiteboul – DBPL 2013 2

• A typical Web user’s data What kinds of data? - all kinds data: photos, music, movies, reports, email metadata: photo taken by Alice in Paris on. . . ontologies: Alice’s ontology and mapping with other ontologies localization: Alice’s pictures are on Picasa, back-ups are at INRIA - security: Facebook credentials (Alice, 123456) Social - annotations: Alice likes Elvis’ website data - beliefs: Alice believes Elvis is alive - external knowledge: Bob keeps copies of Alice’s pictures Abiteboul – DBPL -2013 time, provenance, . . . 3

A typical Web user’s data • • What kinds of data? all kinds Where is the data? everywhere - laptop, desktop, smartphone, tablet, car computer mail, address book, agenda Facebook, Linked. In, Picasa, You. Tube, Tweeter svn, Google docs also access to data / information of family, friends, companies associations Abiteboul – DBPL 2013 4

A typical Web user’s data • • • What kinds of data? all kinds Where is the data? everywhere heterogeneous What kind of organization? - terminology: different ontologies systems: personal machines, social networks distribution: different localization security: different protocols quality: incomplete / inconsistent information Abiteboul – DBPL 2013 5

Example of processing Alice and Bob are getting engaged. Their friends want to offer them an album of photos where they are together To make such a photo album • • Find friends of Alice & Bob (say with Facebook) for each friend, find where she keeps her photos (say, Picassa) • • • find the means to access her photos possibly via friends find the photos that feature Bob and Alice together, e. g. , using tags or face recognition software possibly ask someone to verify the results Some reasoning is needed to execute these tasks automatically! Abiteboul – DBPL 2013 6

A typical Web user • • Overwhelmed by the mass of information Cannot find the information needed Is not aware of important events Cannot manage/control how others access and use his/her own data Abiteboul – DBPL 2013 7

How can systems help? • We need to move from a Web of text to a Web of knowledge • In the spirit of semantic Web To better support user needs, - Systems need to analyze what is happening and construct knowledge Systems should exchange knowledge Systems should reason and infer knowledge YOU need help Abiteboul – DBPL 2013 8

Thesis All this forms a distributed knowledge base with processing based on automated reasoning Abiteboul – DBPL 2013 9

Our topic • Distributed reasoning Exchanging facts and rules Webdamlog • Access control Abiteboul – DBPL 2013 with access 10

• • • The Web as a distributed knowledge base Webdamlog: a rule-based language for the Web Access control in Webdamlog The Webdamlog system Conclusion Abiteboul – DBPL 2013 11

Webdamlog: a datalog-style language Datalog A prehistoric language by Web time. . . + nice and compact syntax + well-studied with many extensions + recursion essential: network cycles Webdamlog Not as simple/beautiful & procedural Needed for real Web applications! Abiteboul – DBPL 2013 12 Webdamlog is not datalog

Webdamlog: an extension of datalog Datalog program fof(x, y) : - friend(x, y) fof(x, y) : - friend(x, z), fof(z, y) Extensional facts (stored in the database) friend(“peter”, ”paul”) friend(“paul”, “mary”) friend(“mary”, ”sue”) Intentional facts (derived) fof(“peter”, ”paul”) fof(“peter”, ”mary”) fof(“peter”, “sue”) fof(“paul”, “mary”) fof(“paul”, “sue”) fof(“mary”, ”sue”) Abiteboul – DBPL 2013 13

Webdamlog: an extension of datalog Extends datalog • negation, updates, distribution, delegation, time For a world that is • • distributed: autonomous and asynchronous peers dynamic: knowledge evolves; peers come and go Influenced by • Active XML (INRIA) - for distribution & intentional data • Dedalus (UC Berkeley) - for time & implementation Abiteboul – DBPL 2013 14

Facts are of the form m@p(a 1, . . . , an), where m is a relation name & p is a peer name a 1, . . . , an are data values (n is the arity of m@p) the set of data values includes the relations and peer names Examples friend@my-iphone(“peter”, “paul”) fof@my-iphone(“adam”, “paul”) Abiteboul – DBPL 2013 15 extensional intentional

Examples of facts data & metadata: pictures@alice-iphone(1771. jpg, “Paris”, 11/11/2011) ontology: is. A@yago. com("Elvis”, the. King) annotations: tags@delicious. com(“wikipedia. org”, encyclopedia) localization: where@alice(pictures, picasa/alice) access rights: right@picasa(pictures, friends, read) security: secret@picasa/alice; public@picasa/alice Abiteboul – DBPL 2013 16

Rules are of the form A term is a variable or a constant $R@$P($U) : - (not) $R 1@$P 1($U 1), . . . , (not) $Rn@$Pn($Un) where $R, $Ri are relation terms $P, $Pi are peer terms $U, $Ui are tuples of terms Safety condition $R and $P must appear positively bound in the body each variable in a negative literal must appear positively bound in the body Abiteboul – DBPL 2013 Examples coming up, stay tuned 17

State transition Choose some peer p randomly – asynchronously Compute the transition of p the database updates at p the messages sent to other peers the delegations of rules to other peers Keep going forever (I 0, Γ 0, ∅) ➝ (I 1, Γ 1*) ➝. . . ➝ (In, Γn*) ➝. . . Fair sequence: each peer is selected infinitely often Abiteboul – DBPL 2013 18

The semantics of rules Classification based on locality and nature of head predicates (intentional or extensional) • Local rule at my-laptop: all predicates in the body of the rules are from my-laptop Local with local intentional head classic datalog Local with local extensional head database update Local with non-local extensional head messaging between peers Local with non-local intentional head view delegation Non-local general delegation Abiteboul – DBPL 2013 19

Local rules with local intentional head Example: Rule at peer my-laptop friend is extensional, fof is intentional fof@my-iphone($x, $y) : - friend@my-iphone($x, $y) fof@my-iphone($x, $y) : - friend@my-iphone($x, $z), fof@myiphone($z, $y) fof is the transitive closure of friend Datalog = Webdamlog with only local rules and local intentional head Abiteboul – DBPL 2013 20

Local rules with local extensional head A new fact is inserted into the local database believe@my-iphone(“Alice”, $loc) : tell@my-iphone($p, ”Alice”, $loc), friend@my-iphone($p) Abiteboul – DBPL 2013 21

Local rules with non-local extensional head A new fact is sent to an external peer via a message $message@$peer($name, “Happy birthday!”) : today@my-iphone($date), birthday@my-iphone($name, $message, $peer, $date) Extensional facts: today@my-iphone(March 6) birthday@my-iphone("Manon”, “sendmail”, “gmail. com”, March sendmail@gmail. com("Manon”, “Happy birthday”) Abiteboul – DBPL 2013 22

Local rules with non-local intentional head View delegation! boy. Meets. Girl@gossip-site($girl, $boy) : girls@my-iphone($girl, $loc), boys@my-iphone($boy, $loc) Semantics of boy. Meet. Girl@gossip-site is a join of relations girls and boys from my-iphone Formally, my-iphone delegates a rule boy. Meet. Girl@gossip-site(g, b) for each g, b, l, girls@my-iphone(g, l), boys@my-iphone(b, l) Abiteboul – DBPL 2013 23

Non-local rules: general delegation (at my-iphone): boy. Meets. Girl@gossip-site($girl, $boy) : girls@my-iphone($girl, $loc), boys@alice-iphone($boy, $loc) Suppose that girls@my-iphone(“Alice”, “Julia's birthday”) holds. Then my-iphone installs the following rule at alice-iphone (at alice-iphone): boy. Meets. Girl@gossip-site(“Alice”, $boy) : boys@alice-iphone($boy, “Julia's birthday”) When girls@my-iphone(“Alice”, “Julia's birthday”) no longer holds, my-iphone uninstalls the rule Abiteboul – DBPL 2013 24

Complexity of delegation: illustration fof(x, y) : - friend(x, y) (at p) fof@p(x, y) : - peers@p($q), friend@$q(x, y) If peers@p contains 100 000 tuples peers@p(q 1), . . , peers@p(q 100 000) This rule will install 100 000 rules! for i=1 to 100 000 (at qi) fof@p(x, y) : friend@qi(x, y) Data complexity transformed into program complexity Abiteboul – DBPL 2013 26

Summary of results [PODS 2011] • • • Formal definition of the semantics of Webdamlog Results on expressivity - the model with delegation is more general, unless all peers and programs are known in advance Convergence is very hard to achieve - positive Webdamlog strongly stratified programs with negation Abiteboul – DBPL 2013 27

• • • The Web as a distributed knowledge base Webdamlog: a rule-based language for the Web Access control in Webdamlog The Webdamlog system Conclusion Abiteboul – DBPL 2013 28

Requirements Data access Users would like to control who can read and modify their information Data dissemination Users would like to control how their data are transferred from one participant to another, and how they are combined, with the owner of each piece of data keeping some control over it Application control Users would like to control which applications can run on their behalf, and what information these applications can access. Abiteboul – DBPL 2013 29

The general picture • • The privileges we consider: read, write, grant For read: • • Coarse grained access control: at the relation level Fine grain access control: at the tuple level Abiteboul – DBPL 2013 30

Insertion in extentional relations Definition of intensional relations • Requires write privilege on the target relation • [at Alice] alice. Photos@Bob($f) : person@Alice($p, “Friend”), person. In. Photo@Alice($pid, $p), photo@Alice($pid, −, $f) • • [at Alice] all. Photos@Alice($f) : alice. Photos@Alice($f) [at Bob] all. Photos@Alice($f) : - bob. Photos@Bob($f) Abiteboul – DBPL 2013 31

Who can read a fact ? – default • • Extensional relations: if you have read privilege to the relation Intensional relations: if you have read privilege to the relation & if you can read all the tuples that have been used to create this fact – provenance of the fact Abiteboul – DBPL 2013 32

Digression: provenance • Provenance of a tuple • • How it was constructed: conjunction Alternatives: disjunction Abiteboul – DBPL 2013 33

Digression: provenance graph (Also used for maintenance in case of update) girls@p(Jane, Julia’s birthday) rule 1 boys@p(John, Julia's birthday) × × rule 3 × + boy. Meets. Girl@p(Jane, John) Abiteboul – DBPL 2013 34 gossip@p(Jane, John) ×

Coarse grain access control • • • [at Alice] alice. Photos@Bob($f) : person@Alice($p, “Friend”), person. In. Photo@Alice($pid, $p), photo@Alice($pid, −, $f) alice. Photos@Bob is extensional Whoever has read access to alice. Photos@Bob sees all the relation Abiteboul – DBPL 2013 35

Fine grain access control • • • [at Alice] all. Photos@Alice($f) : alice. Photos@Alice($f) [at Bob] all. Photos@Alice($f) : - bob. Photos@Bob($f) all. Photos@Alice is intensional Sue who has read privilege to all. Photos@Alice and alice. Photos only, can see only the photos of Alice in all. Photos Lili who has read privilege to the three relations, sees everything Abiteboul – DBPL 2013 36

Overwriting the default for intensional data • • • Let us change the rule to: [at Alice] all. Photos@$x($f) : alice. Photos@Alice($f), friends@Alice($x) Issue: you can read the photos only if you also have read privilege to friends@Alice Abiteboul – DBPL 2013 37

Overwriting the default for intensional data • • • [at Alice] all. Photos@$x($f) : alice. Photos@Alice($f), [hide friends@Alice($x)] Hide: block the provenance from friends@Alice Similar mechanism for extensional data – expose Abiteboul – DBPL 2013 38

• Issues with non local rules [at Bob] message@Sue(“I hate you”) : - date@Alice(d) alice. Secret@Bob(x) : - date@Alice(d), secret@Alice(x) Ignoring access rights, by delegation, this results in running • [at Alice] message@Sue(“I hate you”) : - date@Alice(d) alice. Secret@Bob(x) : - date@Alice(d), secret@Alice(x) Abiteboul – DBPL 2013 39

Default solution: sand box We run the rule at Alice in a Sandbox • We use the access rights of Bob So the second rule does not succeed in sending secrets • The message specifies that this is done at Bob’s request So requires authentication/signatures • Alternative: delegation without sandbox. Possible if the peer that asks for the delegation is given the privilege to install rules at the other peer – Here if Alice gives Bob the right to install a rule in her Abiteboul – DBPL 40 2013 environment

Access control implementation • • A program with access control is compiled locally in a Webdamlog program without that is executed Access control data is managed like any other data Relation acl (defines relation access) Relation kind • • (ext or int) Based on provenance implemented as a distributed graph On-going work on optimization Abiteboul – DBPL 2013 41

• • The Web as a distributed knowledge base Webdamlog: a rule-based language for the Web Access control in Webdamlog The Webdamlog system Abiteboul – DBPL 2013 42

The Webdamlog engine Based on Bud • developed at UC Berkeley Manages knowledge - Stores facts and rules - performs reasoning exchanges knowledge with other engines Abiteboul – DBPL 2013 43

The engine: beyond Bud • Compilation of Webdamlog+AC ⇒ Webdamlog ⇒ Bloom (Bud’s language) • Main Webdamlog features not supported by Bud 1. Variable relation and peer names 2. Delegations with dynamic changes of the program Abiteboul – DBPL 2013 44

The Webdamlog peer Support communication with other peers and with users Support common security protocols Support wrappers to external systems such as Facebook Provides Web interfaces Abiteboul – DBPL 2013 45

Provenance graphs • • • Records the history of derivation Provenance semiring semantics [Green et al. 07] Used for performance optimization Used for fine grain access control Other possible uses such as explanation of results Abiteboul – DBPL 2013 46

• • • The Web as a distributed knowledge base Webdamlog: a rule-based language for the Web Access control in Webdamlog The Webdamlog system Conclusion Abiteboul – DBPL 2013 47

Thesis Let us turn the Web into a distributed knowledge base with billions of users supported by billions of systems analyzing information extracting knowledge exchanging knowledge inferring knowledge Abiteboul – DBPL 2013 48

Language Webdamlog • A language for distributed data management [PODS 2011] • Datalog with distribution, updates, messaging • Main novelty: delegation Implementation • Webdam. Exchange peer in Java [demo ICDE 2011] • Webdamlog engine based on Bud [demo Sigmod 2013] Access control: Stoyanovich on-going work with Miklau- Probabilistic Webdamlog: Abiteboul – DBPL 2013 on-going work with Deutch- 49

Grazie ! Cambridge University Press, 2012 http: //webdam. inria. fr/Jorge