In recent years, there has been much research in Ranked Retrieval model in structured databases, especially those in web databases. With this model, a search query returns top-k tuples according to not just exact matches of selection conditions, but a suitable ranking function. This paper studies a novel problem on the privacy implications of database ranking. The motivation is a novel yet serious privacy leakage we found on real-world web databases which is caused by the ranking function design. Many such databases feature private attributes - e.g., a social network allows users to specify certain attributes as only visible to him herself, but not to others. While these websites generally respect the privacy settings by not directly displaying private attribute values in search query answers, many of them nevertheless take into account such private attributes in the ranking function design. The conventional belief might be that tuple ranks alone are not enough to reveal the private attribute values. Our investigation, however, shows that this is not the case in reality. To address the problem, we introduce a taxonomy of the problem space with two dimensions, (1) the type of query interface and (2) the capability of adversaries. For each subspace, we develop a novel technique which either guarantees the successful inference of private attributes, or does so for a significant portion of real-world tuples. We demonstrate the effectiveness and efficiency of our techniques through theoretical analysis, extensive experiments over real-world datasets, as well as successful online attacks over websites with tens to hundreds of millions of users - e.g., Amazon Goodreads and Renren.com.
Inference Control: Prior work on privacy inference @cite_9 studied the problem of inferring individual tuple values @cite_33 @cite_16 and the existence of a tuple in a database @cite_17 from aggregates such as SUM, MIN, MAX, etc. The field of inference control @cite_9 @cite_19 @cite_18 seeks to prevent such attacks by through query auditing, controlling the number of tuples that match a query or modify query responses using perturbation, distortion etc @cite_30 . Researchers have also proposed multiple privacy preserving aggregate query processing techniques @cite_5 @cite_37 . Recently, @cite_1 has showed that it is possible to infer the location of a user in a Location based Social Network (LBSN) (which could be considered as a private attribute) if the ranking function returns the distance between the query and the victim tuple. However, we do not assume the availability of such information as most websites do not display the score of a tuple for a query.
@cite_30 @cite_18 @cite_37 @cite_33 @cite_9 @cite_1 @cite_19 @cite_5 @cite_16 @cite_17
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We consider the problem of answering queries from databases that may be incomplete. A database is incomplete if some tuples may be missing from some relations, and only a part of each relation is known to be complete. This problem arises in several contexts. For example, systems that provide access to multiple heterogeneous information sources often encounter incomplete sources. The question we address is to determine whether the answer to a specific given query is complete even when the database is incomplete. We present a novel sound and complete algorithm for the answer-completeness problem by relating it to the problem of independence of queries from updates. We also show an important case of the independence problem (and therefore ofthe answer-completeness problem) that can be decided in polynomial time, whereas the best known algorithm for this case is exponential. This case involves updates that are described using a conjunction of comparison predicates. We also describe an algorithm that determines whether the answer to the query is complete in the current state of the database. Finally, we show that our ‘treatment extends naturally to partiallyincorrect databases. Permission to copy without fee all or part of this material is granted provided that the copies aTe not made OT distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, OT to republish, requirea a fee and or special permission from the Endowment. Proceedings of the 22nd VLDB Conference Mumbai(Bombay), India, 1996 Computing aggregate statistics about user data is of vital importance for a variety of services and systems, but this practice has been shown to seriously undermine the privacy of users. Differential privacy has proved to be an effective tool to sanitize queries over a database, and various cryptographic protocols have been recently proposed to enforce differential privacy in a distributed setting, e.g., statical queries on sensitive data stored on the user's side. The widespread deployment of differential privacy techniques in real-life settings is, however, undermined by several limitations that existing constructions suffer from: they support only a limited class of queries, they pose a trade-off between privacy and utility of the query result, they are affected by the answer pollution problem, or they are inefficient. This paper presents PrivaDA, a novel design architecture for distributed differential privacy that leverages recent advances in secure multiparty computations on fixed and floating point arithmetics to overcome the previously mentioned limitations. In particular, PrivaDA supports a variety of perturbation mechanisms (e.g., the Laplace, discrete Laplace, and exponential mechanisms) and it constitutes the first generic technique to generate noise in a fully distributed manner while maintaining the optimal utility. Furthermore, PrivaDA does not suffer from the answer pollution problem. We demonstrate the efficiency of PrivaDA with a performance evaluation, and its expressiveness and flexibility by illustrating several application scenarios such as privacy-preserving web analytics. We prove new upper and lower bounds on the sample complexity of (a#x03B5;, a#x03B4;) differentially private algorithms for releasing approximate answers to threshold functions. A threshold function cx over a totally ordered domain X evaluates to cx(y) = 1 if y a#x2264; x, and evaluates to 0 otherwise. We give the first nontrivial lower bound for releasing thresholds with (a#x03B5;, a#x03B4;) differential privacy, showing that the task is impossible over an infinite domain X, and moreover requires sample complexity n a#x2265; (log* |X|), which grows with the size of the domain. Inspired by the techniques used to prove this lower bound, we give an algorithm for releasing thresholds with n a#x2264; 2(1+o(1)) log* |X| samples. This improves the previous best upper bound of 8(1+o(1)) log* |X| (, RANDOM'13). Our sample complexity upper and lower bounds also apply to the tasks of learning distributions with respect to Kolmogorov distance and of properly PAC learning thresholds with differential privacy. The lower bound gives the first separation between the sample complexity of properly learning a concept class with (a#x03B5;, a#x03B4;) differential privacy and learning without privacy. For properly learning thresholds in 'dimensions, this lower bound extends to n a#x2265; (l a#x2219; log* |X|). To obtain our results, we give reductions in both directions from releasing and properly learning thresholds and the simpler interior point problem. Given a database D of elements from X, the interior point problem asks for an element between the smallest and largest elements in D. We introduce new recursive constructions for bounding the sample complexity of the interior point problem, as well as further reductions and techniques for proving impossibility results for other basic problems in differential privacy. This paper considers the problem of providing security to statistical databases against disclosure of confidential information. Security-control methods suggested in the literature are classified into four general approaches: conceptual, query restriction, data perturbation, and output perturbation. Criteria for evaluating the performance of the various security-control methods are identified. Security-control methods that are based on each of the four approaches are discussed, together with their performance with respect to the identified evaluation criteria. A detailed comparative analysis of the most promising methods for protecting dynamic-online statistical databases is also presented. To date no single security-control method prevents both exact and partial disclosures. There are, however, a few perturbation-based methods that prevent exact disclosure and enable the database administrator to exercise "statistical disclosure control." Some of these methods, however introduce bias into query responses or suffer from the 0 1 query-set-size problem (i.e., partial disclosure is possible in case of null query set or a query set of size 1). We recommend directing future research efforts toward developing new methods that prevent exact disclosure and provide statistical-disclosure control, while at the same time do not suffer from the bias problem and the 0 1 query-set-size problem. Furthermore, efforts directed toward developing a bias-correction mechanism and solving the general problem of small query-set-size would help salvage a few of the current perturbation-based methods.