711993
On the Security of Multiple Encryption or CCA-security+CCA-security=CCA-security?
In a practical system, a message is often encrypted more than once by different encryptions, here called multiple encryption, to enhance its security. Additionally, new features may be achieved by multiple encrypting a message, such as the key-insulated cryptosystems and anonymous channels. Intuitively, a multiple encryption should remain “secure”, whenever there is one component cipher unbreakable in it. In NESSIE’s latest Portfolio of recommended cryptographic primitives (Feb. 2003), it is suggested to use multiple encryption with component ciphers based on different assumptions to acquire long term security. However, in this paper we show this needs careful discussion, especially, this may not be true according to adaptive chosen ciphertext attack (CCA), even with all component ciphers CCA-secure. We define an extended model of (standard) CCA called chosen ciphertext attack for multiple encryption (ME-CCA) emulating partial breaking of assumptions, and give constructions of multiple encryption satisfying ME-CCA-security. We further relax CCA by introducing weak ME-CCA (ME-wCCA) and study the relations among these definitions, proving ME-wCCA-security can be acquired by combining IND-CCA-secure component ciphers together. We then apply these results to key-insulated cryptosystem.
15662668
Orthogonality between Key Privacy and Data Privacy, Revisited
Key privacy is a notion regarding the privacy of the owner of a public key, which has important applications in building (receiver) anonymous channels, or privacy-enhanced authentication/signature schemes. Key privacy is considered to be an orthogonal (i.e., independent), notion from data privacy, while the key privacy of many public key encryption schemes has not been explored, though their data privacy is comparatively well understood. In this paper, we study key privacy of many practical encryption schemes and identify evidences that key privacy is not comparable to data privacy. We also formalize key privacy in the plaintext checking attack model and point out some generic transforms to enhance the key privacy of an encryption scheme. Interestingly, these well-known techniques have been used to enhance data security. Finally, we give detailed security analyses on the signed hashed-ElGamal encryption [27] in the random oracle model, of both key privacy and data security against chosen ciphertext attack. Again, this specific example supports our claim on the relation of two notions.
4696945
Combining Public Key Encryption with Keyword Search and Public Key Encryption
In this paper, we study the problem of secure integrating public key encryption with keyword search (PEKS) with public key data encryption (PKE). We argue the previous security model is not complete regarding keyword privacy and the previous constructions are secure only in the random oracle model. We solve these problems by first defining a new security model, then give a generic construction which is secure in the new security model without random oracles. Our construction is based on secure PEKS and tag-KEM/DEM schemes and achieves modular design. We also give some applications and extensions for our construction. For example, instantiate our construction with proper components, we have a concrete scheme without random oracles, whose performance is even competitive to the previous schemes with random oracles.
39624890
Strong Anonymous Signatures
The notion of anonymous signatures has recently been formalized by [18], which captures an interesting property that a digital signature can sometimes hide the identity of the signer, if the message is hidden from the verifier. However, in many practical applications, e.g., an anonymous paper review system mentioned in [18], the message for anonymous authentication is actually known to the verifier. This implies that the effectiveness of previous anonymous signatures may be unjustified in these applications. In this paper, we extend the previous models, and develop a related primitive called strong anonymous signatures. For strong anonymous signatures, the identity of the signer remains secret even if the challenge message is chosen by an adversary. We then demonstrate some efficient constructions and prove their security in our model.
2552624
Approximate NN queries on Streams with Guaranteed Error/performance Bounds
In data stream applications, data arrive continuously and can only be scanned once as the query processor has very limited memory (relative to the size of the stream) to work with. Hence, queries on data streams do not have access to the entire data set and query answers are typically approximate. While there have been many studies on the k Nearest Neighbors (kNN) problem in conventional multi-dimensional databases, the solutions cannot be directly applied to data streams for the above reasons. In this paper, we investigate the kNN problem over data streams. We first introduce the e-approximate kNN (ekNN) problem that finds the approximate kNN answers of a query point Q such that the absolute error of the k-th nearest neighbor distance is bounded by e. To support ekNN queries over streams, we propose a technique called DISC (aDaptive Indexing on Streams by space-filling Curves). DISC can adapt to different data distributions to either (a) optimize memory utilization to answer ekNN queries under certain accuracy requirements or (b) achieve the best accuracy under a given memory constraint. At the same time, DISC provide efficient updates and query processing which are important requirements in data stream applications. Extensive experiments were conducted using both synthetic and real data sets and the results confirm the effectiveness and efficiency of DISC.
15606709
Mining distribution change in stock order streams
Detecting changes in stock prices is a well known problem in finance with important implications for monitoring and business intelligence. Forewarning of changes in stock price, can be made by the early detection of changes in the distributions of stock order numbers. In this paper, we address the change detection problem for streams of stock order numbers and propose a novel incremental detection algorithm. Our algorithm gains high accuracy and low delay by employing a natural Poisson distribution assumption about the nature of stock order streams. We establish that our algorithm is highly scalable and has linear complexity. We also experimentally demonstrate its effectiveness for detecting change points, via experiments using both synthetic and real-world datasets.
16280855
Indexing Fast Moving Objects for kNN Queries Based on Nearest Landmarks
With the rapid advancements in positioning technologies such as the Global Positioning System (GPS) and wireless communications, the tracking of continuously moving objects has become more convenient. However, this development poses new challenges to database technology since maintaining up-to-date information regarding the location of moving objects incurs an enormous amount of updates. Existing indexes can no longer keep up with the high update rate while providing speedy retrieval at the same time. This study aims to improve k nearest neighbor (kNN) query performance while reducing update costs. Our approach is based on an important observation that queries usually occur around certain places or spatial landmarks of interest, called reference points. We propose the Reference-Point-based tree (RP-tree), which is a two-layer index structure that indexes moving objects according to reference points. Experimental results show that the RP-tree achieves significant improvement over the TPR-tree.
18170201
Streaming multiple aggregations using phantoms
Data streams characterize the high speed and large volume input of a new class of applications such as network monitoring, web content analysis and sensor networks. Among these applications, network monitoring may be the most compelling one—the backbone of a large internet service provider can generate 1 petabyte of data per day. For many network monitoring tasks such as traffic analysis and statistics collection, aggregation is a primitive operation. Various analytical and statistical needs naturally lead to related aggregate queries. In this article, we address the problem of efficiently computing multiple aggregations over high-speed data streams based on the two-level query processing architecture of GS, a real data stream management system deployed in AT & T. We discern that additionally computing and maintaining fine-granularity aggregations (called phantoms) has the benefit of supporting shared computation. Based on a thorough analysis, we propose algorithms to identify the best set of phantoms to maintain and determine allocation of resources (particularly, space) to compute the aggregations. Experiments show that our algorithm achieves near-optimal computation costs, which outperforms the best adapted algorithm by more than an order of magnitude.
36437069
iDistance Techniques