DJoin: Differentially Private Join Queries over Distributed Databases
By Arjun Narayan, Andreas Haeberlen. 2012
134 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Arjun Narayan
- Antonis Papadimitriou
- Noah M. Johnson
- Edo Roth
- Andreas Haeberlen
- Joseph P. Near
- Ariel J. Feldman
- Daniel Noble
Citations in the corpus
- Honeycrisp: large-scale differentially private aggregation without a trusted core (2019, background): “In each case, the underlying technology is quite specific to the class of functions that is being targeted: for instance, the core of [60] is a primitive for setintersection cardinality, which has no obvious connection to the aggregations that Honeycrisp can perform.”
- Differentially Private SQL with Bounded User Contribution (2019, unknown): “, DJoin [39] and Shrinkwrap [2].”
- Verifiable differential privacy (2015, background): “A few recent systems have considered richer threat models: for instance, there are some solutions [14, 33, 36] that can process queries over multiple databases whose curators do not trust each other, while others [10] can prevent malicious subjects from providing data that would distort the results.”
- Distributed Differential Privacy and Applications (2015, methodology): “Fuzz was published in USENIX Security 2011 (Haeberlen, Pierce, and Narayan, 2011), DJoin in OSDI 2012 (Narayan and Haeberlen, 2012), and VerDP in Eurosys 2015 (Narayan, Papadimitriou, and Haeberlen, 2015).”
- Towards Practical Differential Privacy for SQL Queries (2017, background): “DJoin uses a measure of sensitivity unique to its distributed setting.”
- Practical Differential Privacy for SQL Queries Using Elastic Sensitivity (2017, background): “Requirement 1 {}}{ Requirement 2 { }} { Database One-to-one One-to-many Many-to-many compatibility equijoin equijoin equijoin PINQ [27] X wPINQ [33] X X X Restricted sensitivity [10] X X DJoin [29] X Elastic sensitivity (this work) X X X X”
- DStress: Efficient Differentially Private Computations on Distributed Data (2017, background): “Later systems added support for distributed data, but they typically focus on a specific class of queries: for instance, PDDP [16] can build histograms, and DJoin [51] can process certain types of joins.”