Differentially Private Continual Monitoring of Heavy Hitters from Distributed Streams View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2012

AUTHORS

T. -H. Hubert Chan , Mingfei Li , Elaine Shi , Wenchang Xu

ABSTRACT

We consider applications scenarios where an untrusted aggregator wishes to continually monitor the heavy-hitters across a set of distributed streams. Since each stream can contain sensitive data, such as the purchase history of customers, we wish to guarantee the privacy of each stream, while allowing the untrusted aggregator to accurately detect the heavy hitters and their approximate frequencies. Our protocols are scalable in settings where the volume of streaming data is large, since we guarantee low memory usage and processing overhead by each data source, and low communication overhead between the data sources and the aggregator. More... »

PAGES

140-159

Book

TITLE

Privacy Enhancing Technologies

ISBN

978-3-642-31679-1
978-3-642-31680-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-31680-7_8

DOI

http://dx.doi.org/10.1007/978-3-642-31680-7_8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1040107992


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0804", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Data Format", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "The University of Hong Kong, Hong Kong", 
          "id": "http://www.grid.ac/institutes/grid.194645.b", 
          "name": [
            "The University of Hong Kong, Hong Kong"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chan", 
        "givenName": "T. -H. Hubert", 
        "id": "sg:person.010251411300.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010251411300.37"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The University of Hong Kong, Hong Kong", 
          "id": "http://www.grid.ac/institutes/grid.194645.b", 
          "name": [
            "The University of Hong Kong, Hong Kong"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Mingfei", 
        "id": "sg:person.012247710241.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012247710241.37"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "UC Berkeley, USA", 
          "id": "http://www.grid.ac/institutes/grid.47840.3f", 
          "name": [
            "UC Berkeley, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shi", 
        "givenName": "Elaine", 
        "id": "sg:person.014706274717.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014706274717.52"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tsinghua University, China", 
          "id": "http://www.grid.ac/institutes/grid.12527.33", 
          "name": [
            "Tsinghua University, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xu", 
        "givenName": "Wenchang", 
        "id": "sg:person.015315705323.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015315705323.09"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2012", 
    "datePublishedReg": "2012-01-01", 
    "description": "We consider applications scenarios where an untrusted aggregator wishes to continually monitor the heavy-hitters across a set of distributed streams. Since each stream can contain sensitive data, such as the purchase history of customers, we wish to guarantee the privacy of each stream, while allowing the untrusted aggregator to accurately detect the heavy hitters and their approximate frequencies. Our protocols are scalable in settings where the volume of streaming data is large, since we guarantee low memory usage and processing overhead by each data source, and low communication overhead between the data sources and the aggregator.", 
    "editor": [
      {
        "familyName": "Fischer-H\u00fcbner", 
        "givenName": "Simone", 
        "type": "Person"
      }, 
      {
        "familyName": "Wright", 
        "givenName": "Matthew", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-31680-7_8", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-642-31679-1", 
        "978-3-642-31680-7"
      ], 
      "name": "Privacy Enhancing Technologies", 
      "type": "Book"
    }, 
    "keywords": [
      "untrusted aggregator", 
      "heavy hitters", 
      "low memory usage", 
      "data sources", 
      "Distributed Streams", 
      "sensitive data", 
      "low communication", 
      "application scenarios", 
      "memory usage", 
      "purchase history", 
      "aggregator", 
      "hitters", 
      "streams", 
      "privacy", 
      "communication", 
      "customers", 
      "scenarios", 
      "usage", 
      "set", 
      "protocol", 
      "data", 
      "approximate frequency", 
      "continual monitoring", 
      "monitoring", 
      "source", 
      "setting", 
      "volume", 
      "frequency", 
      "history", 
      "Private Continual Monitoring"
    ], 
    "name": "Differentially Private Continual Monitoring of Heavy Hitters from Distributed Streams", 
    "pagination": "140-159", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1040107992"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-31680-7_8"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-31680-7_8", 
      "https://app.dimensions.ai/details/publication/pub.1040107992"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2021-11-01T18:49", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211101/entities/gbq_results/chapter/chapter_188.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-642-31680-7_8"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-31680-7_8'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-31680-7_8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-31680-7_8'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-31680-7_8'


 

This table displays all metadata directly associated to this object as RDF triples.

122 TRIPLES      23 PREDICATES      56 URIs      49 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-31680-7_8 schema:about anzsrc-for:08
2 anzsrc-for:0804
3 schema:author N3d00f7c6d25846d1aeb26a6eb437a94f
4 schema:datePublished 2012
5 schema:datePublishedReg 2012-01-01
6 schema:description We consider applications scenarios where an untrusted aggregator wishes to continually monitor the heavy-hitters across a set of distributed streams. Since each stream can contain sensitive data, such as the purchase history of customers, we wish to guarantee the privacy of each stream, while allowing the untrusted aggregator to accurately detect the heavy hitters and their approximate frequencies. Our protocols are scalable in settings where the volume of streaming data is large, since we guarantee low memory usage and processing overhead by each data source, and low communication overhead between the data sources and the aggregator.
7 schema:editor N0636ae60ccdf4f07892279bb662b05ac
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree true
11 schema:isPartOf N45e96d0abc714499b87e24d64b74fda3
12 schema:keywords Distributed Streams
13 Private Continual Monitoring
14 aggregator
15 application scenarios
16 approximate frequency
17 communication
18 continual monitoring
19 customers
20 data
21 data sources
22 frequency
23 heavy hitters
24 history
25 hitters
26 low communication
27 low memory usage
28 memory usage
29 monitoring
30 privacy
31 protocol
32 purchase history
33 scenarios
34 sensitive data
35 set
36 setting
37 source
38 streams
39 untrusted aggregator
40 usage
41 volume
42 schema:name Differentially Private Continual Monitoring of Heavy Hitters from Distributed Streams
43 schema:pagination 140-159
44 schema:productId N3b42545f530f4b88b1bb55687bab39e6
45 N82bae39b563041899be43f6e6235185b
46 schema:publisher Nd01c79e05e644d89adb068b728822c1d
47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040107992
48 https://doi.org/10.1007/978-3-642-31680-7_8
49 schema:sdDatePublished 2021-11-01T18:49
50 schema:sdLicense https://scigraph.springernature.com/explorer/license/
51 schema:sdPublisher N5f1783e3de6f46158e3cdc88125900bd
52 schema:url https://doi.org/10.1007/978-3-642-31680-7_8
53 sgo:license sg:explorer/license/
54 sgo:sdDataset chapters
55 rdf:type schema:Chapter
56 N0636ae60ccdf4f07892279bb662b05ac rdf:first N15ff6dd161604d36bafbde3aa4e2c805
57 rdf:rest Nf5c0fd0ac0a14f4fb170bbd88f7307e7
58 N08d97f7d9fb642bc902b2249b1a736fe rdf:first sg:person.012247710241.37
59 rdf:rest N1cb5afc589754c34906a876e68c628ff
60 N0ebf77c5188b495f88a594b36668fbb2 schema:familyName Wright
61 schema:givenName Matthew
62 rdf:type schema:Person
63 N15ff6dd161604d36bafbde3aa4e2c805 schema:familyName Fischer-Hübner
64 schema:givenName Simone
65 rdf:type schema:Person
66 N1cb5afc589754c34906a876e68c628ff rdf:first sg:person.014706274717.52
67 rdf:rest N8a32b3f43cfc44aab71c580d6ba2bc3b
68 N3b42545f530f4b88b1bb55687bab39e6 schema:name doi
69 schema:value 10.1007/978-3-642-31680-7_8
70 rdf:type schema:PropertyValue
71 N3d00f7c6d25846d1aeb26a6eb437a94f rdf:first sg:person.010251411300.37
72 rdf:rest N08d97f7d9fb642bc902b2249b1a736fe
73 N45e96d0abc714499b87e24d64b74fda3 schema:isbn 978-3-642-31679-1
74 978-3-642-31680-7
75 schema:name Privacy Enhancing Technologies
76 rdf:type schema:Book
77 N5f1783e3de6f46158e3cdc88125900bd schema:name Springer Nature - SN SciGraph project
78 rdf:type schema:Organization
79 N82bae39b563041899be43f6e6235185b schema:name dimensions_id
80 schema:value pub.1040107992
81 rdf:type schema:PropertyValue
82 N8a32b3f43cfc44aab71c580d6ba2bc3b rdf:first sg:person.015315705323.09
83 rdf:rest rdf:nil
84 Nd01c79e05e644d89adb068b728822c1d schema:name Springer Nature
85 rdf:type schema:Organisation
86 Nf5c0fd0ac0a14f4fb170bbd88f7307e7 rdf:first N0ebf77c5188b495f88a594b36668fbb2
87 rdf:rest rdf:nil
88 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
89 schema:name Information and Computing Sciences
90 rdf:type schema:DefinedTerm
91 anzsrc-for:0804 schema:inDefinedTermSet anzsrc-for:
92 schema:name Data Format
93 rdf:type schema:DefinedTerm
94 sg:person.010251411300.37 schema:affiliation grid-institutes:grid.194645.b
95 schema:familyName Chan
96 schema:givenName T. -H. Hubert
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010251411300.37
98 rdf:type schema:Person
99 sg:person.012247710241.37 schema:affiliation grid-institutes:grid.194645.b
100 schema:familyName Li
101 schema:givenName Mingfei
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012247710241.37
103 rdf:type schema:Person
104 sg:person.014706274717.52 schema:affiliation grid-institutes:grid.47840.3f
105 schema:familyName Shi
106 schema:givenName Elaine
107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014706274717.52
108 rdf:type schema:Person
109 sg:person.015315705323.09 schema:affiliation grid-institutes:grid.12527.33
110 schema:familyName Xu
111 schema:givenName Wenchang
112 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015315705323.09
113 rdf:type schema:Person
114 grid-institutes:grid.12527.33 schema:alternateName Tsinghua University, China
115 schema:name Tsinghua University, China
116 rdf:type schema:Organization
117 grid-institutes:grid.194645.b schema:alternateName The University of Hong Kong, Hong Kong
118 schema:name The University of Hong Kong, Hong Kong
119 rdf:type schema:Organization
120 grid-institutes:grid.47840.3f schema:alternateName UC Berkeley, USA
121 schema:name UC Berkeley, USA
122 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...