17 research outputs found
Semi-Trusted Mixer Based Privacy Preserving Distributed Data Mining for Resource Constrained Devices
In this paper a homomorphic privacy preserving association rule mining
algorithm is proposed which can be deployed in resource constrained devices
(RCD). Privacy preserved exchange of counts of itemsets among distributed
mining sites is a vital part in association rule mining process. Existing
cryptography based privacy preserving solutions consume lot of computation due
to complex mathematical equations involved. Therefore less computation involved
privacy solutions are extremely necessary to deploy mining applications in RCD.
In this algorithm, a semi-trusted mixer is used to unify the counts of itemsets
encrypted by all mining sites without revealing individual values. The proposed
algorithm is built on with a well known communication efficient association
rule mining algorithm named count distribution (CD). Security proofs along with
performance analysis and comparison show the well acceptability and
effectiveness of the proposed algorithm. Efficient and straightforward privacy
model and satisfactory performance of the protocol promote itself among one of
the initiatives in deploying data mining application in RCD.Comment: IEEE Publication format, International Journal of Computer Science
and Information Security, IJCSIS, Vol. 8 No. 1, April 2010, USA. ISSN 1947
5500, http://sites.google.com/site/ijcsis
CROSS SPACE OPINION ARRANGEMENT UTILIZING ASSUMPTION TOUCHY INSERTING'S IN INFORMATION MINING
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Privacy-preserving Cross-domain Routing Optimization -- A Cryptographic Approach
Today's large-scale enterprise networks, data center networks, and wide area
networks can be decomposed into multiple administrative or geographical
domains. Domains may be owned by different administrative units or
organizations. Hence protecting domain information is an important concern.
Existing general-purpose Secure Multi-Party Computation (SMPC) methods that
preserves privacy for domains are extremely slow for cross-domain routing
problems. In this paper we present PYCRO, a cryptographic protocol specifically
designed for privacy-preserving cross-domain routing optimization in Software
Defined Networking (SDN) environments. PYCRO provides two fundamental routing
functions, policy-compliant shortest path computing and bandwidth allocation,
while ensuring strong protection for the private information of domains. We
rigorously prove the privacy guarantee of our protocol. We have implemented a
prototype system that runs PYCRO on servers in a campus network. Experimental
results using real ISP network topologies show that PYCRO is very efficient in
computation and communication costs
The misty crystal ball: Efficient concealment of privacy-sensitive attributes in predictive analytics
Individuals are becoming increasingly concerned with privacy. This curtails their willingness to share sensitive attributes like age, gender or personal preferences; yet firms largely rely upon customer data in any type of predictive analytics. Hence, organizations are confronted with a dilemma in which they need to make a tradeoff between a sparse use of data and the utility from better predictive analytics. This paper proposes a masking mechanism that obscures sensitive attributes while maintaining a large degree of predictive power. More precisely, we efficiently identify data partitions that are best suited for (i) shuffling, (ii) swapping and, as a form of randomization, (iii) perturbing attributes by conditional replacement. By operating on data partitions that are derived from a predictive algorithm, we achieve the objective of masking privacy-sensitive attributes with marginal downsides for predictive modeling. The resulting trade-off between masking and predictive utility is empirically evaluated in the context of customer churn where, for instance, a stratified shuffling of attribute values impedes predictive accuracy rarely by more than a percentage point. Our proposed framework entails direct managerial implications as a growing share of firms adopts predictive analytics and thus requires mechanisms that better adhere to user demands for information privacy
Secure equality testing protocols in the two-party setting
Protocols for securely testing the equality of two encrypted integers are common building blocks for a number of proposals in the literature that aim for privacy preservation. Being used repeatedly in many cryptographic protocols, designing efficient equality testing protocols is important in terms of computation and communication overhead. In this work, we consider a scenario with two parties where party A has two integers encrypted using an additively homomorphic scheme and party B has the decryption key. Party A would like to obtain an encrypted bit that shows whether the integers are equal or not but nothing more. We propose three secure equality testing protocols, which are more efficient in terms of communication, computation or both compared to the existing work. To support our claims, we present experimental results, which show that our protocols achieve up to 99% computation-wise improvement compared to the state-of-the-art protocols in a fair experimental set-up
Privacy preserving distributed optimization using homomorphic encryption
This paper studies how a system operator and a set of agents securely execute
a distributed projected gradient-based algorithm. In particular, each
participant holds a set of problem coefficients and/or states whose values are
private to the data owner. The concerned problem raises two questions: how to
securely compute given functions; and which functions should be computed in the
first place. For the first question, by using the techniques of homomorphic
encryption, we propose novel algorithms which can achieve secure multiparty
computation with perfect correctness. For the second question, we identify a
class of functions which can be securely computed. The correctness and
computational efficiency of the proposed algorithms are verified by two case
studies of power systems, one on a demand response problem and the other on an
optimal power flow problem.Comment: 24 pages, 5 figures, journa
Privacy-Preserving Aggregation of Time-Series Data
The conference paper can be viewed at: http://www.isoc.org/isoc/conferences/ndss/11/proceedings.shtmlSession 9: PrivacyWe consider how an untrusted data aggregator can
learn desired statistics over multiple participants’ data,
without compromising each individual’s privacy. We
propose a construction that allows a group of participants
to periodically upload encrypted values to a data
aggregator, such that the aggregator is able to compute
the sum of all participants’ values in every time period,
but is unable to learn anything else. We achieve strong
privacy guarantees using two main techniques. First, we
show how to utilize applied cryptographic techniques to
allow the aggregator to decrypt the sum from multiple
ciphertexts encrypted under different user keys. Second,
we describe a distributed data randomization procedure
that guarantees the differential privacy of the outcome
statistic, even when a subset of participants might be
compromised.published_or_final_versio
CryptDB: A Practical Encrypted Relational DBMS
CryptDB is a DBMS that provides provable and practical privacy in the face of a compromised database server or curious database administrators. CryptDB works by executing SQL queries over encrypted data. At its core are three novel ideas: an SQL-aware encryption strategy that maps SQL operations to encryption schemes, adjustable query-based encryption which allows CryptDB to adjust the encryption level of each data item based on user queries, and onion encryption to efficiently change data encryption levels. CryptDB only empowers the server to execute queries that the users requested, and achieves maximum privacy given the mix of queries issued by the users. The database server fully evaluates queries on encrypted data and sends the result back to the client for final decryption; client machines do not perform any query processing and client-side applications run unchanged. Our evaluation shows that CryptDB has modest overhead: on the TPC-C benchmark on Postgres, CryptDB reduces throughput by 27% compared to regular Postgres. Importantly, CryptDB does not change the innards of existing DBMSs: we realized the implementation of CryptDB using client-side query rewriting/encrypting, user-defined functions, and server-side tables for public key information. As such, CryptDB is portable; porting CryptDB to MySQL required changing 86 lines of code, mostly at the connectivity layer
Privacy-Preserving Aggregation of Time-Series Data
The conference paper can be viewed at: http://www.isoc.org/isoc/conferences/ndss/11/proceedings.shtmlSession 9: PrivacyWe consider how an untrusted data aggregator can
learn desired statistics over multiple participants’ data,
without compromising each individual’s privacy. We
propose a construction that allows a group of participants
to periodically upload encrypted values to a data
aggregator, such that the aggregator is able to compute
the sum of all participants’ values in every time period,
but is unable to learn anything else. We achieve strong
privacy guarantees using two main techniques. First, we
show how to utilize applied cryptographic techniques to
allow the aggregator to decrypt the sum from multiple
ciphertexts encrypted under different user keys. Second,
we describe a distributed data randomization procedure
that guarantees the differential privacy of the outcome
statistic, even when a subset of participants might be
compromised.published_or_final_versio