43,792 research outputs found
Conditionals in Homomorphic Encryption and Machine Learning Applications
Homomorphic encryption aims at allowing computations on encrypted data
without decryption other than that of the final result. This could provide an
elegant solution to the issue of privacy preservation in data-based
applications, such as those using machine learning, but several open issues
hamper this plan. In this work we assess the possibility for homomorphic
encryption to fully implement its program without relying on other techniques,
such as multiparty computation (SMPC), which may be impossible in many use
cases (for instance due to the high level of communication required). We
proceed in two steps: i) on the basis of the structured program theorem
(Bohm-Jacopini theorem) we identify the relevant minimal set of operations
homomorphic encryption must be able to perform to implement any algorithm; and
ii) we analyse the possibility to solve -- and propose an implementation for --
the most fundamentally relevant issue as it emerges from our analysis, that is,
the implementation of conditionals (requiring comparison and selection/jump
operations). We show how this issue clashes with the fundamental requirements
of homomorphic encryption and could represent a drawback for its use as a
complete solution for privacy preservation in data-based applications, in
particular machine learning ones. Our approach for comparisons is novel and
entirely embedded in homomorphic encryption, while previous studies relied on
other techniques, such as SMPC, demanding high level of communication among
parties, and decryption of intermediate results from data-owners. Our protocol
is also provably safe (sharing the same safety as the homomorphic encryption
schemes), differently from other techniques such as
Order-Preserving/Revealing-Encryption (OPE/ORE).Comment: 14 pages, 1 figure, corrected typos, added introductory pedagogical
section on polynomial approximatio
Forward Private Searchable Symmetric Encryption with Optimized I/O Efficiency
Recently, several practical attacks raised serious concerns over the security
of searchable encryption. The attacks have brought emphasis on forward privacy,
which is the key concept behind solutions to the adaptive leakage-exploiting
attacks, and will very likely to become mandatory in the design of new
searchable encryption schemes. For a long time, forward privacy implies
inefficiency and thus most existing searchable encryption schemes do not
support it. Very recently, Bost (CCS 2016) showed that forward privacy can be
obtained without inducing a large communication overhead. However, Bost's
scheme is constructed with a relatively inefficient public key cryptographic
primitive, and has a poor I/O performance. Both of the deficiencies
significantly hinder the practical efficiency of the scheme, and prevent it
from scaling to large data settings. To address the problems, we first present
FAST, which achieves forward privacy and the same communication efficiency as
Bost's scheme, but uses only symmetric cryptographic primitives. We then
present FASTIO, which retains all good properties of FAST, and further improves
I/O efficiency. We implemented the two schemes and compared their performance
with Bost's scheme. The experiment results show that both our schemes are
highly efficient, and FASTIO achieves a much better scalability due to its
optimized I/O
Cloud-based Quadratic Optimization with Partially Homomorphic Encryption
The development of large-scale distributed control systems has led to the
outsourcing of costly computations to cloud-computing platforms, as well as to
concerns about privacy of the collected sensitive data. This paper develops a
cloud-based protocol for a quadratic optimization problem involving multiple
parties, each holding information it seeks to maintain private. The protocol is
based on the projected gradient ascent on the Lagrange dual problem and
exploits partially homomorphic encryption and secure multi-party computation
techniques. Using formal cryptographic definitions of indistinguishability, the
protocol is shown to achieve computational privacy, i.e., there is no
computationally efficient algorithm that any involved party can employ to
obtain private information beyond what can be inferred from the party's inputs
and outputs only. In order to reduce the communication complexity of the
proposed protocol, we introduced a variant that achieves this objective at the
expense of weaker privacy guarantees. We discuss in detail the computational
and communication complexity properties of both algorithms theoretically and
also through implementations. We conclude the paper with a discussion on
computational privacy and other notions of privacy such as the non-unique
retrieval of the private information from the protocol outputs
Privacy-Preserving and Outsourced Multi-User k-Means Clustering
Many techniques for privacy-preserving data mining (PPDM) have been
investigated over the past decade. Often, the entities involved in the data
mining process are end-users or organizations with limited computing and
storage resources. As a result, such entities may want to refrain from
participating in the PPDM process. To overcome this issue and to take many
other benefits of cloud computing, outsourcing PPDM tasks to the cloud
environment has recently gained special attention. We consider the scenario
where n entities outsource their databases (in encrypted format) to the cloud
and ask the cloud to perform the clustering task on their combined data in a
privacy-preserving manner. We term such a process as privacy-preserving and
outsourced distributed clustering (PPODC). In this paper, we propose a novel
and efficient solution to the PPODC problem based on k-means clustering
algorithm. The main novelty of our solution lies in avoiding the secure
division operations required in computing cluster centers altogether through an
efficient transformation technique. Our solution builds the clusters securely
in an iterative fashion and returns the final cluster centers to all entities
when a pre-determined termination condition holds. The proposed solution
protects data confidentiality of all the participating entities under the
standard semi-honest model. To the best of our knowledge, ours is the first
work to discuss and propose a comprehensive solution to the PPODC problem that
incurs negligible cost on the participating entities. We theoretically estimate
both the computation and communication costs of the proposed protocol and also
demonstrate its practical value through experiments on a real dataset.Comment: 16 pages, 2 figures, 5 table
Body language, security and e-commerce
Security is becoming an increasingly more important concern both at the desktop level and at the network level. This article discusses several approaches to authenticating individuals through the use of biometric devices. While libraries might not implement such devices, they may appear in the near future of desktop computing, particularly for access to institutional computers or for access to sensitive information. Other approaches to computer security focus on protecting the contents of electronic transmissions and verification of individual users. After a brief overview of encryption technologies, the article examines public-key cryptography which is getting a lot of attention in the business world in what is called public key infrastructure. It also examines other efforts, such as IBM’s Cryptolope, the Secure Sockets Layer of Web browsers, and Digital Certificates and Signatures. Secure electronic transmissions are an important condition for conducting business on the Net. These business transactions are not limited to purchase orders, invoices, and contracts. This could become an important tool for information vendors and publishers to control access to the electronic resources they license. As license negotiators and contract administrators, librarians need to be aware of what is happening in these new technologies and the impact that will have on their operations
Secret charing vs. encryption-based techniques for privacy preserving data mining
Privacy preserving querying and data publishing has been studied in the context of statistical databases and statistical disclosure control. Recently, large-scale data collection and integration efforts increased privacy concerns which motivated data mining researchers to investigate privacy implications of data mining and how data mining can be performed without violating privacy. In this paper, we first provide an overview of privacy preserving data mining focusing on distributed data sources, then we compare two technologies used in privacy preserving data mining. The first technology is encryption based, and it is used in earlier approaches. The second technology is secret-sharing which is recently being considered as a more efficient approach
- …