Communications of the ACM
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Sharing Decryption in the Context of Voting or Lotteries
FC '00 Proceedings of the 4th International Conference on Financial Cryptography
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
CarTel: a distributed mobile sensor computing system
Proceedings of the 4th international conference on Embedded networked sensor systems
Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The BikeNet mobile sensing system for cyclist experience mapping
Proceedings of the 5th international conference on Embedded networked sensor systems
Time series compressibility and privacy
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
The boundary between privacy and utility in data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
PoolView: stream privacy for grassroots participatory sensing
Proceedings of the 6th ACM conference on Embedded network sensor systems
Relationship privacy: output perturbation for queries with joins
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A survey of computational location privacy
Personal and Ubiquitous Computing
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
Our data, ourselves: privacy via distributed noise generation
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Differentially-private network trace analysis
Proceedings of the ACM SIGCOMM 2010 conference
Privacy-aware regression modeling of participatory sensing data
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Differentially private data cubes: optimizing noise sources and consistency
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
iReduct: differential privacy with reduced relative errors
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
I have a DREAM!: differentially private smart metering
IH'11 Proceedings of the 13th international conference on Information hiding
Sharing graphs using differentially private graph models
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
GUPT: privacy preserving data analysis made easy
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Differential privacy in data publication and analysis
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Towards statistical queries over distributed private user data
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Low-rank mechanism: optimizing batch queries under differential privacy
Proceedings of the VLDB Endowment
Functional mechanism: regression analysis under differential privacy
Proceedings of the VLDB Endowment
Differentially private continual monitoring of heavy hitters from distributed streams
PETS'12 Proceedings of the 12th international conference on Privacy Enhancing Technologies
Fault-tolerant privacy-preserving statistics
PETS'12 Proceedings of the 12th international conference on Privacy Enhancing Technologies
Privacy-aware personalization for mobile advertising
Proceedings of the 2012 ACM conference on Computer and communications security
Proceedings of the 2012 ACM conference on Computer and communications security
DJoin: differentially private join queries over distributed databases
OSDI'12 Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation
Real-time aggregate monitoring with differential privacy
Proceedings of the 21st ACM international conference on Information and knowledge management
Optimal lower bound for differentially private multi-party aggregation
ESA'12 Proceedings of the 20th Annual European conference on Algorithms
Specialization in i* strategic rationale diagrams
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
On differentially private frequent itemset mining
Proceedings of the VLDB Endowment
Differential private trajectory protection of moving objects
Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming
Efficient and accurate strategies for differentially-private sliding window queries
Proceedings of the 16th International Conference on Extending Database Technology
Secure multiparty aggregation with differential privacy: a comparative study
Proceedings of the Joint EDBT/ICDT 2013 Workshops
FAST: differentially private real-time aggregate monitor with filtering and adaptive sampling
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Utility-maximizing event stream suppression
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
SplitX: high-performance private analytics
Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
Practical differential privacy via grouping and smoothing
Proceedings of the VLDB Endowment
Haze: privacy-preserving real-time traffic statistics
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Privacy vulnerability of published anonymous mobility traces
IEEE/ACM Transactions on Networking (TON)
Differentially private multi-dimensional time series release for traffic monitoring
DBSec'13 Proceedings of the 27th international conference on Data and Applications Security and Privacy XXVII
Differential privacy based on importance weighting
Machine Learning
Private aggregation for presence streams
Future Generation Computer Systems
Monitoring web browsing behavior with differential privacy
Proceedings of the 23rd international conference on World wide web
Differentially private histogram publication
The VLDB Journal — The International Journal on Very Large Data Bases
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We propose the first differentially private aggregation algorithm for distributed time-series data that offers good practical utility without any trusted server. This addresses two important challenges in participatory data-mining applications where (i) individual users collect temporally correlated time-series data (such as location traces, web history, personal health data), and (ii) an untrusted third-party aggregator wishes to run aggregate queries on the data. To ensure differential privacy for time-series data despite the presence of temporal correlation, we propose the Fourier Perturbation Algorithm (FPAk). Standard differential privacy techniques perform poorly for time-series data. To answer n queries, such techniques can result in a noise of Θ(n) to each query answer, making the answers practically useless if n is large. Our FPAk algorithm perturbs the Discrete Fourier Transform of the query answers. For answering n queries, FPAk improves the expected error from Θ(n) to roughly Θ(k) where k is the number of Fourier coefficients that can (approximately) reconstruct all the n query answers. Our experiments show that k n for many real-life data-sets resulting in a huge error-improvement for FPAk. To deal with the absence of a trusted central server, we propose the Distributed Laplace Perturbation Algorithm (DLPA) to add noise in a distributed way in order to guarantee differential privacy. To the best of our knowledge, DLPA is the first distributed differentially private algorithm that can scale with a large number of users: DLPA outperforms the only other distributed solution for differential privacy proposed so far, by reducing the computational load per user from O(U) to O(1) where U is the number of users.