STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
The dining cryptographers problem: unconditional sender and recipient untraceability
Journal of Cryptology
Verifiable secret sharing and multiparty protocols with honest majority
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Comparing the combinational complexities of arithmetic functions
Journal of the ACM (JACM)
Multi party computations: past and present
PODC '97 Proceedings of the sixteenth annual ACM symposium on Principles of distributed computing
A new public key cryptosystem based on higher residues
CCS '98 Proceedings of the 5th ACM conference on Computer and communications security
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Non-interactive Private Auctions
FC '01 Proceedings of the 5th International Conference on Financial Cryptography
Privacy-Preserving Cooperative Statistical Analysis
ACSAC '01 Proceedings of the 17th Annual Computer Security Applications Conference
Privacy-Preserving Cooperative Scientific Computations
CSFW '01 Proceedings of the 14th IEEE workshop on Computer Security Foundations
A study of several specific secure two-party computation problems
A study of several specific secure two-party computation problems
Secure distributed data-mining and its application to large-scale network measurements
ACM SIGCOMM Computer Communication Review
Privacy-preserving performance measurements
Proceedings of the 2006 SIGCOMM workshop on Mining network data
A privacy-preserving interdomain audit framework
Proceedings of the 5th ACM workshop on Privacy in electronic society
Transforming semi-honest protocols to ensure accountability
Data & Knowledge Engineering
Building a privacy-preserving benchmarking enterprise system
Enterprise Information Systems - Challenges and Solutions in Enterprise Computing - 11th International IEEE EDOC Conference (EDOC 2007)
On the practical importance of communication complexity for secure multi-party computation protocols
Proceedings of the 2009 ACM symposium on Applied Computing
Secure Multi-party Computation Using Virtual Parties for Computation on Encrypted Data
ISA '09 Proceedings of the 3rd International Conference and Workshops on Advances in Information Security and Assurance
A Privacy-Preserving Platform for User-Centric Quantitative Benchmarking
TrustBus '09 Proceedings of the 6th International Conference on Trust, Privacy and Security in Digital Business
Multiple target localisation in sensor networks with location privacy
ESAS'07 Proceedings of the 4th European conference on Security and privacy in ad-hoc and sensor networks
TMA'11 Proceedings of the Third international conference on Traffic monitoring and analysis
N-gram based secure similar document detection
DBSec'11 Proceedings of the 25th annual IFIP WG 11.3 conference on Data and applications security and privacy
Secure and efficient protocols for iris and fingerprint identification
ESORICS'11 Proceedings of the 16th European conference on Research in computer security
Filtering for private collaborative benchmarking
ETRICS'06 Proceedings of the 2006 international conference on Emerging Trends in Information and Communication Security
Secure computation with fixed-point numbers
FC'10 Proceedings of the 14th international conference on Financial Cryptography and Data Security
Computer Networks: The International Journal of Computer and Telecommunications Networking
EsPRESSO: Efficient privacy-preserving evaluation of sample set similarity
Journal of Computer Security
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Suppose a number of hospitals in a geographic area want to learn how their own heart-surgery unit is doing compared with the others in terms of mortality rates, subsequent complications, or any other quality metric. Similarly, a number of small businesses might want to use their recent point-of-sales data to cooperatively forecast future demand and thus make more informed decisions about inventory, capacity, employment, etc. These are simple examples of cooperative benchmarking and (respectively) forecasting that would benefit all participants as well as the public at large, as they would make it possible for participants to avail themselves of more precise and reliable data collected from many sources, to assess their own local performance in comparison to global trends, and to avoid many of the inefficiencies that currently arise because of having less information available for their decision-making. And yet, in spite of all these advantages, cooperative benchmarking and forecasting typically do not take place, because of the participants' unwillingness to share their information with others. Their reluctance to share is quite rational, and is due to fears of embarrassment, lawsuits, weakening their negotiating position (e.g., in case of over-capacity), revealing corporate performance and strategies, etc. The development and deployment of private benchmarking and forecasting technologies would allow such collaborations to take place without revealing any participant's data to the others, reaping the benefits of collaboration while avoiding the drawbacks. Moreover, this kind of technology would empower smaller organizations who could then cooperatively base their decisions on a much broader information base, in a way that is today restricted to only the largest corporations. This paper is a step towards this goal, as it gives protocols for forecasting and benchmarking that reveal to the participants the desired answers yet do not reveal to any participant any other participant's private data. We consider several forecasting methods, including linear regression and time series techniques such as moving average and exponential smoothing. One of the novel parts of this work, that further distinguishes it from previous work in secure multi-party computation, is that it involves floating point arithmetic, in particular it provides protocols to securely and efficiently perform division.