Multiparty Reach and Frequency Histogram: Private, Secure, and Practical

Abstract Consider the setting where multiple parties each hold a multiset of users and the task is to estimate the reach (i.e., the number of distinct users appearing across all parties) and the frequency histogram (i.e., fraction of users appearing a given number of times across all parties). In this work we introduce a new sketch for this task, based on an exponentially distributed counting Bloom filter. We combine this sketch with a communication-efficient multi-party protocol to solve the task in the multi-worker setting. Our protocol exhibits both differential privacy and security guarantees in the honest-but-curious model and in the presence of large subsets of colluding workers; furthermore, its reach and frequency histogram estimates have a provably small error. Finally, we show the practicality of the protocol by evaluating it on internet-scale audiences.

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The Variable-Increment Counting Bloom Filter

IEEE/ACM Transactions on Networking ◽

10.1109/tnet.2013.2272604 ◽

2014 ◽

Vol 22 (4) ◽

pp. 1092-1105 ◽

Cited By ~ 38

Author(s):

Ori Rottenstreich ◽

Yossi Kanizo ◽

Isaac Keslassy

Keyword(s):

Bloom Filter ◽

Counting Bloom Filter

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Improving counting Bloom filter performance with fingerprints

Information Processing Letters ◽

10.1016/j.ipl.2015.11.002 ◽

2016 ◽

Vol 116 (4) ◽

pp. 304-309 ◽

Cited By ~ 13

Author(s):

Salvatore Pontarelli ◽

Pedro Reviriego ◽

Juan Antonio Maestro

Keyword(s):

Bloom Filter ◽

Filter Performance ◽

Counting Bloom Filter

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An Authentication Key Agreement Scheme for Heterogeneous Sensor Network Based on Improved Counting Bloom Filter

2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC) ◽

10.1109/3pgcic.2015.119 ◽

2015 ◽

Cited By ~ 1

Author(s):

Jiuru Wang ◽

Haifeng Wang ◽

Xu An Wang ◽

Yunpeng Cao

Keyword(s):

Sensor Network ◽

Key Agreement ◽

Bloom Filter ◽

Heterogeneous Sensor Network ◽

Authentication Key Agreement ◽

Counting Bloom Filter

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Continuous K Nearest Neighbor Query Scheme with Privacy and Security Guarantees in Road Networks

2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) ◽

10.1109/smartworld.2018.00153 ◽

2018 ◽

Author(s):

Changli Zhou ◽

Tian Wang ◽

Hui Tian ◽

Wenxian Jiang

Keyword(s):

Nearest Neighbor ◽

Road Networks ◽

K Nearest Neighbor ◽

Privacy And Security ◽

Nearest Neighbor Query ◽

Security Guarantees

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Multi-Granularities Counting Bloom Filter

High Performance Computing and Communications - Lecture Notes in Computer Science ◽

10.1007/11847366_56 ◽

2006 ◽

pp. 542-551

Author(s):

Zhou Mingzhong ◽

Gong Jian ◽

Ding Wei ◽

Cheng Guang

Keyword(s):

Bloom Filter ◽

Counting Bloom Filter

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Parloom: A New Low-Power Set-Associative Instruction Cache Architecture Utilizing Enhanced Counting Bloom Filter and Partial Tags

Journal of Circuits System and Computers ◽

10.1142/s0218126619502037 ◽

2019 ◽

Vol 28 (12) ◽

pp. 1950203

Author(s):

Sajjad Rostami-Sani ◽

Mojtaba Valinataj ◽

Saeideh Alinezhad Chamazcoti

Keyword(s):

Energy Consumption ◽

System Performance ◽

Bloom Filter ◽

Instruction Cache ◽

Cache Architecture ◽

Power Set ◽

Novel Method ◽

Instruction Caches ◽

Cache System ◽

Counting Bloom Filter

The cache system dissipates a significant amount of energy compared to the other memory components. This will be intensified if a cache is designed with a set-associative structure to improve the system performance because the parallel accesses to the entries of a set for tag comparisons lead to even more energy consumption. In this paper, a novel method is proposed as a combination of a counting Bloom filter and partial tags to mitigate the energy consumption of set-associative caches. This new hybrid method noticeably decreases the cache energy consumption especially in highly-associative instruction caches. In fact, it uses an enhanced counting Bloom filter to predict cache misses with a high accuracy as well as partial tags to decrease the overall cache size. This way, unnecessary tag comparisons can be prevented and therefore, the cache energy consumption is considerably reduced. Based on the simulation results, the proposed method provides the energy reduction from 22% to 31% for 4-way–32-way set-associative L1 caches bigger than 16[Formula: see text]kB running the MiBench programs. The improvements are attained with a negligible system performance degradation compared to the traditional cache system.

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