Private queries in location based services: anonymizers are not necessary

By Gabriel Ghinita, Panos Kalnis, Ali Khoshgozaran, Cyrus Shahabi, Kian-Lee Tan. SIGMOD Conference, 2008

893 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/c1339458836db3645c9fe29362e180ae84a9e206

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ian Goldberg
2. Yu Zheng
3. Konstantinos Chatzikokolakis
4. Catuscia Palamidessi
5. Nicolás Emilio Bordenabe
6. Li Xiong
7. Yonghui Xiao
8. Xiaofang Zhou

In Computer Science Applications

1. Yu Zheng
2. Xiaofang Zhou
3. Li Xiong
4. Yonghui Xiao
5. Hien To
6. Nikos Mamoulis
7. Catuscia Palamidessi
8. Dan Boneh

Citations in the corpus

• PrivCheck: privacy-preserving check-in data publishing for personalized location based services (2016, background): “A typical example is private location based query [13], where a user does not want to reveal her location information when sending a location-based query.”
• Achieving Efficient Query Privacy for Location Based Services (2010, methodology): “We note that a similar experiment in [13] considers a much smaller database; only 10,000 and 100,000 POIs.”
• Protecting Locations with Differential Privacy under Temporal Correlations (2014, background): “PIR based meth-ods (e.g. [22, 41, 44, 55]) guarantee cryptographic privacy by allowing data retrieval from a database without revealing any information to the database server about the retrieved item.”
• Secure kNN computation on encrypted databases (2009, background): “In particular, they choose a common encryption scheme.”
• Geo-indistinguishability: differential privacy for location-based systems (2012, background): “can be mapped back to spatial information only by the user [24, 20].”
• Location Privacy via Private Proximity Testing (2011, positive): “show how to use Private Information Retrieval in Location-based services such as search [15].”
• Privacy preservation in wireless sensor networks: A state-of-the-art survey (2009, background): “In particular, a node closer to the base station (i.e., data collecting and processing server) has to relay data from nodes further away from base station in addition to transmitting its own generated data, leading to higher transmission rate.”
• A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing (2014, background): “However, the SC-server may not be trusted, and disclosing individual locations has serious privacy implications [10, 22, 8, 4].”
• Computing with Spatial Trajectories (2011, background): “Due to the novel application domains along with advancements in database technology, a lot of recent research has been undertaken, addressing problems in modelling and efficient querying of imprecise/uncertain data [2, 20, 111, 125, 133, 134].”