Cited By
View all- Yao XZhang RHuang DZhang Y(2021)Verifiable Query Processing Over Outsourced Social GraphIEEE/ACM Transactions on Networking10.1109/TNET.2021.308557429:5(2313-2326)Online publication date: 18-Jun-2021
AuthPDB: Authentication of Probabilistic Queries on Outsourced Uncertain Data
Pages 121 - 132
Abstract
Query processing over uncertain data has gained much attention recently. Due to the high computational complexity of query evaluation on uncertain data, the data owner can outsource her data to a server that provides query evaluation as a service. However, a dishonest server may return cheap (and incorrect) query answers, hoping that the client who has weak computational power cannot catch the incorrect results. To address the integrity issue, in this paper, we design AuthPDB, a framework that supports efficient authentication of query evaluation for both all-answer and top-k queries on outsourced probabilistic databases. Our empirical results on real-world datasets demonstrate the effectiveness and efficiency of AuthPDB.
References
[1]
Charu C Aggarwal and S Yu Philip. 2009. A survey of uncertain data algorithms and applications. IEEE Transactions on Knowledge and Data Engineering, Vol. 21, 5 (2009), 609--623.
[2]
Lyublena Antova, Thomas Jansen, Christoph Koch, and Dan Olteanu. 2008. Fast and simple relational processing of uncertain data. In IEEE International Conference on Data Engineering (ICDE). 983--992.
[3]
Ismail Ilkan Ceylan, Stefan Borgwardt, and Thomas Lukasiewicz. 2017. Most Probable Explanations for Probabilistic Database Queries. In International Joint Conference on Artificial Intelligence. 950--956. https://doi.org/10.24963/ijcai.2017/132
[4]
Surajit Chaudhuri, Kris Ganjam, Venkatesh Ganti, and Rajeev Motwani. 2003. Robust and efficient fuzzy match for online data cleaning. In ACM International Conference on Management of Data (SIGMOD). 313--324.
[5]
Nilesh N. Dalvi and Dan Suciu. 2004. Efficient Query Evaluation on Probabilistic Databases. In Proceedings of the Thirtieth International Conference on Very Large Data Bases . 864--875.
[6]
Ivan Bjerre Damgård. 1987. Collision free hash functions and public key signature schemes. In Workshop on the Theory and Application of of Cryptographic Techniques. Springer, 203--216.
[7]
Premkumar Devanbu, Michael Gertz, Charles Martel, and Stuart G Stubblebine. 2003. Authentic data publication over the Internet. Journal of Computer Security, Vol. 11, 3 (2003), 291--314.
[8]
D Eastlake 3rd and Paul Jones. 2001. US secure hash algorithm 1 (SHA1) . Technical Report.
[9]
Norbert Fuhr and Thomas Rölleke. 1997. A Probabilistic Relational Algebra for the Integration of Information Retrieval and Database Systems. ACM Transactions on Information Systems, Vol. 15, 1 (Jan. 1997), 32--66. https://doi.org/10.1145/239041.239045
[10]
Michael R Garey and David S Johnson. 2002. Computers and intractability . Vol. 29. wh freeman New York.
[11]
Antonin Guttman. 1984. R-trees: A dynamic index structure for spatial searching. Vol. 14. ACM.
[12]
Ihab F Ilyas, George Beskales, and Mohamed A Soliman. 2008. A survey of top-k query processing techniques in relational database systems. ACM Computing Surveys (CSUR), Vol. 40, 4 (2008), 11.
[13]
FlatWorld Inc. [n.d.] a. FlatWorld believes that instructors are the key to quality student education. https://catalog.flatworldknowledge.com.
[14]
ThermoFisher Scientific Inc. [n.d.] b. Lab Data Management & Analysis Software. https://www.thermofisher.com/us/en/home.html.
[15]
Dmitri V Kalashnikov and Sharad Mehrotra. 2006. Domain-independent data cleaning via analysis of entity-relationship graph. ACM Transactions on Database Systems (TODS), Vol. 31, 2 (2006), 716--767.
[16]
Bhargav Kanagal, Jian Li, and Amol Deshpande. 2011. Sensitivity analysis and explanations for robust query evaluation in probabilistic databases. In Proceedings of the International Conference on Management of data. ACM, 841--852.
[17]
Younghoon Kim and Kyuseok Shim. 2012. Parallel top-k similarity join algorithms using MapReduce. In International Conference on Data Engineering (ICDE). IEEE, 510--521.
[18]
Feifei Li, Marios Hadjieleftheriou, George Kollios, and Leonid Reyzin. 2006. Dynamic authenticated index structures for outsourced databases. In ACM International Conference on Management of Data (SIGMOD). 121--132.
[19]
Feifei Li, Marios Hadjieleftheriou, George Kollios, and Leonid Reyzin. 2010. Authenticated index structures for aggregation queries. ACM Transactions on Information and System Security (TISSEC) (2010), 32.
[20]
Kyriakos Mouratidis, Dimitris Sacharidis, and Hweehwa Pang. 2009. Partially materialized digest scheme: an efficient verification method for outsourced databases. International Journal on Very Large Data Bases, Vol. 18, 1 (2009), 363--381.
[21]
Einar Mykletun, Maithili Narasimha, and Gene Tsudik. 2006. Authentication and integrity in outsourced databases. ACM Transactions on Storage (TOS), Vol. 2, 2 (2006), 107--138.
[22]
Dimitrios Papadopoulos, Stavros Papadopoulos, and Nikos Triandopoulos. 2014. Taking authenticated range queries to arbitrary dimensions. In Conference on Computer and Communications Security (CCS). 819--830.
[23]
Charalampos Papamanthou, Roberto Tamassia, and Nikos Triandopoulos. 2011. Optimal verification of operations on dynamic sets. In Annual Cryptology Conference . 91--110.
[24]
R.C.Merkle. 1980. Protocols for public key cryptosystems. In Symposium on Security and Privacy (S&P) .
[25]
Christopher Re, Nilesh Dalvi, and Dan Suciu. 2007. Efficient top-k query evaluation on probabilistic data. In IEEE International Conference on Data Engineering (ICDE). 886--895.
[26]
Ronald L Rivest, Adi Shamir, and Leonard Adleman. 1978. A method for obtaining digital signatures and public-key cryptosystems. Commun. ACM, Vol. 21, 2 (1978), 120--126.
[27]
Sandeepkumar Satpal and Sunita Sarawagi. 2007. Domain adaptation of conditional probability models via feature subsetting. In European Conference on Principles of Data Mining and Knowledge Discovery . 224--235.
[28]
Mohamed A Soliman, Ihab F Ilyas, and Kevin Chen-Chuan Chang. 2007. Top-k query processing in uncertain databases. In IEEE International Conference on Data Engineering. 896--905.
[29]
Federal trade commission. [n.d.]. https://www.consumer.ftc.gov/articles/0079-rental-listing-scams.
[30]
Vijay V Vazirani. 2013. Approximation algorithms .Springer Science & Business Media.
[31]
Jennifer Widom. 2008. Trio: A system for data, uncertainty, and lineage. Managing and Mining Uncertain Data, Vol. 35 (2008), 1--35.
[32]
Zhengyuan Xue, Ruixuan Li, Heng Zhang, Xiwu Gu, and Zhiyong Xu. 2016. DC-Top-k: A Novel Top-k Selecting Algorithm and Its Parallelization. In International Conference on Parallel Processing. IEEE, 370--379.
[33]
Yin Yang, Dimitris Papadias, Stavros Papadopoulos, and Panos Kalnis. 2009. Authenticated join processing in outsourced databases. In Proceedings of the International Conference on Management of data (SIGMOD). 5--18.
[34]
Bo Zhang, Boxiang Dong, and Hui Wang. 2019 a. AuthPDB: Query Authentication for Outsourced Probabilistic Databases. (2019). https://msuweb.montclair.edu/ dongb/publications/authpdb2019-full.pdf
[35]
Bo Zhang, Boxiang Dong, and Wendy Hui Wang. 2019 b. Integrity Authentication for SQL Query Evaluation on Outsourced Databases: A Survey. IEEE Transactions on Knowledge and Data Engineering (2019).
[36]
Xi Zhang and Jan Chomicki. 2009. Semantics and evaluation of top-k queries in probabilistic databases. Distributed and Parallel Databases, Vol. 26, 1 (2009), 67--126.
[37]
Yupeng Zhang, Jonathan Katz, and Charalampos Papamanthou. 2015. Integridb: Verifiable SQL for outsourced databases. In Conference on Computer and Communications Security (CCS). 1480--1491.
Index Terms
- AuthPDB: Authentication of Probabilistic Queries on Outsourced Uncertain Data
Recommendations
Scrubbing query results from probabilistic databases
IDEAS '11: Proceedings of the 15th Symposium on International Database Engineering & ApplicationsQueries over probabilistic databases lead to probabilistic results. As the process of arriving at these results is based on underlying data probabilities, we believe involving a user in the loop of query processing and leveraging the user's personal ...
Conjunctive Queries on Probabilistic Graphs: Combined Complexity
PODS '17: Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database SystemsQuery evaluation over probabilistic databases is known to be intractable in many cases, even in data complexity, i.e., when the query is fixed. Although some restrictions of the queries and instances[4] have been proposed to lower the complexity, these ...
Semantics and evaluation of top-k queries in probabilistic databases
We study here fundamental issues involved in top-k query evaluation in probabilistic databases. We consider simple probabilistic databases in which probabilities are associated with individual tuples, and general probabilistic databases in which, ...
Comments
Information & Contributors
Information
Published In
March 2020
392 pages
ISBN:9781450371070
DOI:10.1145/3374664
- General Chairs:
- Vassil Roussev,
- Bhavani Thuraisingham,
- Program Chairs:
- Barbara Carminati,
- Murat Kantarcioglu
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 March 2020
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
Conference
CODASPY '20
Sponsor:
CODASPY '20: Tenth ACM Conference on Data and Application Security and Privacy
March 16 - 18, 2020
LA, New Orleans, USA
Acceptance Rates
Overall Acceptance Rate 149 of 789 submissions, 19%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 201Total Downloads
- Downloads (Last 12 months)46
- Downloads (Last 6 weeks)13
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
Cited By
View all- Yao XZhang RHuang DZhang Y(2021)Verifiable Query Processing Over Outsourced Social GraphIEEE/ACM Transactions on Networking10.1109/TNET.2021.308557429:5(2313-2326)Online publication date: 18-Jun-2021
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in